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ORCIDAnima Anandkumar
Animashree Anandkumar
Person information
- affiliation: California Institute of Technology, Pasadena, USA
- affiliation: NVIDIA, USA
- affiliation (former): University of California Irvine, Center for Pervasive Communications and Computing
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2020 – today
- 2024
[j57]Rafal Kocielnik, Zhuofang Li, Claudia Kann, Deshawn Sambrano, Jacob Morrier, Mitchell Linegar, Carly Taylor, Min Kim, Nabiha Naqvie, Feri Soltani, Arman Dehpanah, Grant Cahill, Animashree Anandkumar, R. Michael Alvarez:
Challenges in moderating disruptive player behavior in online competitive action games. Frontiers Comput. Sci. 6 (2024)- [j56]Bokui Shen
, Zhenyu Jiang, Christopher Bongsoo Choy, Silvio Savarese, Leonidas J. Guibas, Anima Anandkumar, Yuke Zhu:
Action-conditional implicit visual dynamics for deformable object manipulation. Int. J. Robotics Res. 43(4): 437-455 (2024) - [j55]Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Animashree Anandkumar:
State-specific protein-ligand complex structure prediction with a multiscale deep generative model. Nat. Mac. Intell. 6(2): 195-208 (2024) - [j54]Jie Feng, Yuanyuan Shi, Guannan Qu, Steven H. Low, Anima Anandkumar, Adam Wierman:
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control. IEEE Trans. Control. Netw. Syst. 11(3): 1370-1381 (2024) - [j53]Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar:
Prismer: A Vision-Language Model with Multi-Task Experts. Trans. Mach. Learn. Res. 2024 (2024) - [j52]Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar:
Voyager: An Open-Ended Embodied Agent with Large Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [j51]Hongkai Zheng, Weili Nie, Arash Vahdat, Anima Anandkumar:
Fast Training of Diffusion Models with Masked Transformers. Trans. Mach. Learn. Res. 2024 (2024) - [c204]Shuaiyi Huang, De-An Huang, Zhiding Yu, Shiyi Lan, Subhashree Radhakrishnan, José M. Álvarez, Abhinav Shrivastava, Anima Anandkumar:
What is Point Supervision Worth in Video Instance Segmentation? CVPR Workshops 2024: 2671-2681 - [c203]Zetong Yang, Zhiding Yu, Christopher B. Choy, Renhao Wang, Anima Anandkumar, José M. Álvarez:
Improving Distant 3D Object Detection Using 2D Box Supervision. CVPR 2024: 14853-14863 - [c202]Chulin Xie, De-An Huang, Wenda Chu, Daguang Xu, Chaowei Xiao, Bo Li, Anima Anandkumar:
Perada: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees. CVPR 2024: 23838-23848 - [c201]Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli:
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. ICLR 2024 - [c200]Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, Anima Anandkumar:
Eureka: Human-Level Reward Design via Coding Large Language Models. ICLR 2024 - [c199]Renbo Tu, Colin White, Jean Kossaifi, Boris Bonev, Gennady Pekhimenko, Kamyar Azizzadenesheli, Anima Anandkumar:
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators. ICLR 2024 - [c198]Sihyun Yu, Weili Nie, De-An Huang, Boyi Li, Jinwoo Shin, Anima Anandkumar:
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition. ICLR 2024 - [c197]Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu:
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training. ICML 2024 - [c196]Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar:
Neural Operators with Localized Integral and Differential Kernels. ICML 2024 - [c195]Logan Murphy, Kaiyu Yang, Jialiang Sun, Zhaoyu Li, Anima Anandkumar, Xujie Si:
Autoformalizing Euclidean Geometry. ICML 2024 - [c194]Hong Chul Nam, Julius Berner, Anima Anandkumar:
Solving Poisson Equations using Neural Walk-on-Spheres. ICML 2024 - [c193]Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar:
Equivariant Graph Neural Operator for Modeling 3D Dynamics. ICML 2024 - [c192]Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, Yuandong Tian:
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection. ICML 2024 - [c191]Renhao Wang, Zhiding Yu, Shiyi Lan, Enze Xie, Ke Chen, Anima Anandkumar, José M. Álvarez:
SF3D: SlowFast Temporal 3D Object Detection. IV 2024: 1280-1285 - [c190]Zelun Luo, Yuliang Zou, Yijin Yang, Zane Durante, De-An Huang, Zhiding Yu, Chaowei Xiao, Li Fei-Fei, Animashree Anandkumar:
Differentially Private Video Activity Recognition. WACV 2024: 6643-6653 - [i270]Bingyin Zhao, Zhiding Yu, Shiyi Lan, Yutao Cheng, Anima Anandkumar, Yingjie Lao, José M. Álvarez:
Fully Attentional Networks with Self-emerging Token Labeling. CoRR abs/2401.03844 (2024) - [i269]Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar:
Equivariant Graph Neural Operator for Modeling 3D Dynamics. CoRR abs/2401.11037 (2024) - [i268]Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Vignesh C. Bhethanabotla, Nakul Rampal, Omar Yaghi, Christian Borgs, Anima Anandkumar, Hongyu Guo, Jennifer T. Chayes:
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics. CoRR abs/2401.15122 (2024) - [i267]Ziqi Ma, Kamyar Azizzadenesheli, Anima Anandkumar:
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction. CoRR abs/2402.01960 (2024) - [i266]Pengrui Han, Rafal Kocielnik, Adhithya Prakash Saravanan, Roy Jiang, Or Sharir, Anima Anandkumar:
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs. CoRR abs/2402.11764 (2024) - [i265]Zizheng Pan, Bohan Zhuang, De-An Huang, Weili Nie, Zhiding Yu, Chaowei Xiao, Jianfei Cai, Anima Anandkumar:
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching. CoRR abs/2402.14167 (2024) - [i264]Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar:
Neural Operators with Localized Integral and Differential Kernels. CoRR abs/2402.16845 (2024) - [i263]Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, Yuandong Tian:
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection. CoRR abs/2403.03507 (2024) - [i262]Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu:
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training. CoRR abs/2403.03542 (2024) - [i261]Zetong Yang, Zhiding Yu, Christopher B. Choy, Renhao Wang, Anima Anandkumar, José M. Álvarez:
Improving Distant 3D Object Detection Using 2D Box Supervision. CoRR abs/2403.09230 (2024) - [i260]Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel V. Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar:
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs. CoRR abs/2403.12553 (2024) - [i259]Sihyun Yu, Weili Nie, De-An Huang, Boyi Li, Jinwoo Shin, Anima Anandkumar:
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition. CoRR abs/2403.14148 (2024) - [i258]Shuaiyi Huang, De-An Huang, Zhiding Yu, Shiyi Lan, Subhashree Radhakrishnan, José M. Álvarez, Abhinav Shrivastava, Anima Anandkumar:
What is Point Supervision Worth in Video Instance Segmentation? CoRR abs/2404.01990 (2024) - [i257]Peiyang Song, Kaiyu Yang, Anima Anandkumar:
Towards Large Language Models as Copilots for Theorem Proving in Lean. CoRR abs/2404.12534 (2024) - [i256]Logan Murphy, Kaiyu Yang, Jialiang Sun, Zhaoyu Li, Anima Anandkumar, Xujie Si:
Autoformalizing Euclidean Geometry. CoRR abs/2405.17216 (2024) - [i255]Hong Chul Nam, Julius Berner, Anima Anandkumar:
Solving Poisson Equations using Neural Walk-on-Spheres. CoRR abs/2406.03494 (2024) - [i254]Shuaiyi Huang, Mara Levy, Zhenyu Jiang, Anima Anandkumar, Yuke Zhu, Linxi Fan, De-An Huang, Abhinav Shrivastava:
ARDuP: Active Region Video Diffusion for Universal Policies. CoRR abs/2406.13301 (2024) - [i253]Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song:
Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. CoRR abs/2407.01521 (2024) - [i252]Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Müller, Kamyar Azizzadenesheli, Anima Anandkumar:
Dynamical Measure Transport and Neural PDE Solvers for Sampling. CoRR abs/2407.07873 (2024) - [i251]Cheng Luo, Jiawei Zhao, Zhuoming Chen, Beidi Chen, Anima Anandkumar:
MINI-SEQUENCE TRANSFORMER: Optimizing Intermediate Memory for Long Sequences Training. CoRR abs/2407.15892 (2024) - [i250]Chuwei Wang, Julius Berner, Zongyi Li, Di Zhou, Jiayun Wang, Jane Bae, Anima Anandkumar:
Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators. CoRR abs/2408.05177 (2024) - [i249]Freya Shah, Taylor L. Patti, Julius Berner, Bahareh Tolooshams, Jean Kossaifi, Anima Anandkumar:
Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems. CoRR abs/2409.03302 (2024) - [i248]Shengchao Liu, Divin Yan, Weitao Du, Weiyang Liu, Zhuoxinran Li, Hongyu Guo, Christian Borgs, Jennifer T. Chayes, Anima Anandkumar:
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design. CoRR abs/2409.10584 (2024) - [i247]Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang:
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. CoRR abs/2409.18433 (2024) - 2023
- [j50]Andrew J. Hung, Richard Bao, Idris O. Sunmola, De-An Huang, Jessica H. Nguyen, Anima Anandkumar:
Capturing fine-grained details for video-based automation of suturing skills assessment. Int. J. Comput. Assist. Radiol. Surg. 18(3): 545-552 (2023) - [j49]Abigail C. Dommer, Lorenzo Casalino, Fiona L. Kearns, Mia Rosenfeld, Nicholas Wauer, Surl-Hee Ahn, John Russo, A. Sofia F. Oliveira, Clare Morris, Anthony T. Bogetti, Anda Trifan, Alexander Brace, Terra Sztain, Austin Clyde, Heng Ma, S. Chakra Chennubhotla, Hyungro Lee, Matteo Turilli, Syma Khalid, Teresa Tamayo-Mendoza, Matthew Welborn, Anders S. Christensen, Daniel G. A. Smith, Zhuoran Qiao, Sai K. Sirumalla, Michael O'Connor, Frederick R. Manby, Anima Anandkumar, David J. Hardy, James C. Phillips, Abraham C. Stern, Josh Romero, David Clark, Mitchell Dorrell, Tom Maiden, Lei Huang, John D. McCalpin, Christopher J. Woods, Alan Gray, Matt Williams, Bryan Barker, Harinda Rajapaksha, Richard Pitts, Tom Gibbs, John E. Stone, Daniel M. Zuckerman, Adrian J. Mulholland, Thomas F. Miller III, Shantenu Jha, Arvind Ramanathan, Lillian T. Chong, Rommie E. Amaro:
#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. Int. J. High Perform. Comput. Appl. 37(1): 28-44 (2023) - [j48]Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, Defne G. Ozgulbas, Natalia Vassilieva, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan:
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. Int. J. High Perform. Comput. Appl. 37(6): 683-705 (2023) - [j47]Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs. J. Mach. Learn. Res. 24: 89:1-89:97 (2023) - [j46]Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar:
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries. J. Mach. Learn. Res. 24: 388:1-388:26 (2023) - [j45]Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Animashree Anandkumar:
Multi-modal molecule structure-text model for text-based retrieval and editing. Nat. Mac. Intell. 5(12): 1447-1457 (2023) - [j44]Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora S. Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, Marinka Zitnik:
Scientific discovery in the age of artificial intelligence. Nat. 620(7972): 47-60 (2023) - [j43]Dani Kiyasseh, Jasper Laca, Taseen F. Haque, Maxwell X. Otiato, Brian J. Miles, Christian Wagner, Daniel A. Donoho, Quoc-Dien Trinh, Animashree Anandkumar, Andrew J. Hung:
Human visual explanations mitigate bias in AI-based assessment of surgeon skills. npj Digit. Medicine 6 (2023) - [j42]Taylor L. Patti, Jean Kossaifi, Anima Anandkumar, Susanne F. Yelin:
Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints. Quantum 7: 1057 (2023) - [j41]Yuji Roh, Weili Nie, De-An Huang, Steven Euijong Whang, Arash Vahdat, Anima Anandkumar:
Dr-Fairness: Dynamic Data Ratio Adjustment for Fair Training on Real and Generated Data. Trans. Mach. Learn. Res. 2023 (2023) - [c189]Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, Animashree Anandkumar:
Distributionally Robust Policy Gradient for Offline Contextual Bandits. AISTATS 2023: 6443-6462 - [c188]Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi:
Thompson Sampling for Partially Observable Linear-Quadratic Control. ACC 2023: 4561-4568 - [c187]Benyamin Allahgholizadeh Haghi, Lin Ma, Sahin Lale, Anima Anandkumar, Azita Emami:
EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification. BioCAS 2023: 1-5 - [c186]Sahin Lale, Yuanyuan Shi, Guannan Qu, Kamyar Azizzadenesheli, Adam Wierman, Anima Anandkumar:
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Discrete-Time Systems. CDC 2023: 1334-1341 - [c185]Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar:
MimicPlay: Long-Horizon Imitation Learning by Watching Human Play. CoRL 2023: 201-221 - [c184]Wei Dong, Christopher B. Choy, Charles Loop, Or Litany, Yuke Zhu, Anima Anandkumar:
Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids. CVPR 2023: 4263-4272 - [c183]Yiming Li, Zhiding Yu, Christopher B. Choy, Chaowei Xiao, José M. Álvarez, Sanja Fidler, Chen Feng, Anima Anandkumar:
VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion. CVPR 2023: 9087-9098 - [c182]Shiyi Lan, Xitong Yang, Zhiding Yu, Zuxuan Wu, José M. Álvarez, Anima Anandkumar:
Vision Transformers are Good Mask Auto-Labelers. CVPR 2023: 23745-23755 - [c181]Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro:
Context Generation Improves Open Domain Question Answering. EACL (Findings) 2023: 781-796 - [c180]Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro:
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study. EMNLP 2023: 7763-7786 - [c179]Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar:
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning. EMNLP (Findings) 2023: 11844-11857 - [c178]Adhithya Prakash Saravanan, Rafal Kocielnik, Roy Jiang, Pengrui Han, Anima Anandkumar:
Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models. ICBINB 2023: 84-102 - [c177]Bingyin Zhao, Zhiding Yu, Shiyi Lan, Yutao Cheng, Anima Anandkumar, Yingjie Lao, José M. Álvarez:
Fully Attentional Networks with Self-emerging Token Labeling. ICCV 2023: 5562-5572 - [c176]Zhiqi Li, Zhiding Yu, Wenhai Wang, Anima Anandkumar, Tong Lu, José M. Álvarez:
FB-BEV: BEV Representation from Forward-Backward View Transformations. ICCV 2023: 6896-6905 - [c175]Yilun Chen, Zhiding Yu, Yukang Chen, Shiyi Lan, Anima Anandkumar, Jiaya Jia, José M. Álvarez:
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection. ICCV 2023: 8360-8371 - [c174]Jaesung Choe, Christopher B. Choy, Jaesik Park, In So Kweon, Anima Anandkumar:
Spacetime Surface Regularization for Neural Dynamic Scene Reconstruction. ICCV 2023: 17825-17835 - [c173]Yanwei Li, Zhiding Yu, Jonah Philion, Anima Anandkumar, Sanja Fidler, Jiaya Jia, Jose Alvarez:
End-to-end 3D Tracking with Decoupled Queries. ICCV 2023: 18256-18265 - [c172]Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard G. Baraniuk, Anima Anandkumar:
Retrieval-based Controllable Molecule Generation. ICLR 2023 - [c171]Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song:
DensePure: Understanding Diffusion Models for Adversarial Robustness. ICLR 2023 - [c170]Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar:
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. ICML 2023: 2806-2823 - [c169]Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, Linxi Fan:
VIMA: Robot Manipulation with Multimodal Prompts. ICML 2023: 14975-15022 - [c168]Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A. Theodorou, Weili Nie, Anima Anandkumar:
I2SB: Image-to-Image Schrödinger Bridge. ICML 2023: 22042-22062 - [c167]Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, Anima Anandkumar:
Fast Sampling of Diffusion Models via Operator Learning. ICML 2023: 42390-42402 - [c166]Haoxuan Wang, Zhiding Yu, Yisong Yue, Animashree Anandkumar, Anqi Liu, Junchi Yan:
Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach. IJCAI 2023: 1460-1469 - [c165]Anima Anandkumar:
Neural Operators for Solving PDEs and Inverse Design. ISPD 2023: 195 - [c164]Rafal Kocielnik, Elyssa Y. Wong, Timothy N. Chu, Lydia Lin, De-An Huang, Jiayun Wang, Anima Anandkumar, Andrew J. Hung:
Deep Multimodal Fusion for Surgical Feedback Classification. ML4H@NeurIPS 2023: 256-267 - [c163]Zongyi Li, Nikola B. Kovachki, Christopher B. Choy, Boyi Li, Jean Kossaifi, Shourya Prakash Otta, Mohammad Amin Nabian, Maximilian Stadler, Christian Hundt, Kamyar Azizzadenesheli, Animashree Anandkumar:
Geometry-Informed Neural Operator for Large-Scale 3D PDEs. NeurIPS 2023 - [c162]Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhi-Ming Ma, Omar Yaghi, Animashree Anandkumar, Christian Borgs, Jennifer T. Chayes, Hongyu Guo, Jian Tang:
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials. NeurIPS 2023 - [c161]Kaiyu Yang, Aidan M. Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan J. Prenger, Animashree Anandkumar:
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models. NeurIPS 2023 - [c160]Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard:
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS 2023 - [c159]Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Anima Anandkumar:
FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. PASC 2023: 13:1-13:11 - [d2]Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Animashree Anandkumar:
NeuralPLexer evaluation datasets and predictions. Zenodo, 2023
- [i246]Shiyi Lan, Xitong Yang, Zhiding Yu, Zuxuan Wu, José M. Álvarez, Anima Anandkumar:
Vision Transformers Are Good Mask Auto-Labelers. CoRR abs/2301.03992 (2023) - [i245]Peter I Renn, Cong Wang, Sahin Lale, Zongyi Li, Anima Anandkumar, Morteza Gharib:
Forecasting subcritical cylinder wakes with Fourier Neural Operators. CoRR abs/2301.08290 (2023) - [i244]Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar:
A Text-guided Protein Design Framework. CoRR abs/2302.04611 (2023) - [i243]Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Ming-Yu Liu, Yuke Zhu, Mohammad Shoeybi, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar:
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning. CoRR abs/2302.04858 (2023) - [i242]Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A. Theodorou, Weili Nie, Anima Anandkumar:
I2SB: Image-to-Image Schrödinger Bridge. CoRR abs/2302.05872 (2023) - [i241]Chulin Xie, De-An Huang, Wenda Chu, Daguang Xu, Chaowei Xiao, Bo Li, Anima Anandkumar:
PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees. CoRR abs/2302.06637 (2023) - [i240]Rafal Kocielnik, Shrimai Prabhumoye, Vivian Zhang, R. Michael Alvarez, Anima Anandkumar:
AutoBiasTest: Controllable Sentence Generation for Automated and Open-Ended Social Bias Testing in Language Models. CoRR abs/2302.07371 (2023) - [i239]Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar:
Score-based Diffusion Models in Function Space. CoRR abs/2302.07400 (2023) - [i238]Yiming Li, Zhiding Yu, Christopher B. Choy, Chaowei Xiao, José M. Álvarez, Sanja Fidler, Chen Feng, Anima Anandkumar:
VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion. CoRR abs/2302.12251 (2023) - [i237]Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar:
MimicPlay: Long-Horizon Imitation Learning by Watching Human Play. CoRR abs/2302.12422 (2023) - [i236]Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar:
Prismer: A Vision-Language Model with An Ensemble of Experts. CoRR abs/2303.02506 (2023) - [i235]Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro:
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study. CoRR abs/2304.06762 (2023) - [i234]Wei Dong, Christopher B. Choy, Charles Loop, Or Litany, Yuke Zhu, Anima Anandkumar:
Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids. CoRR abs/2305.13220 (2023) - [i233]Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar:
Voyager: An Open-Ended Embodied Agent with Large Language Models. CoRR abs/2305.16291 (2023) - [i232]Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli:
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. CoRR abs/2305.18246 (2023) - [i231]Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar:
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. CoRR abs/2306.03838 (2023) - [i230]Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C. Will, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Anima Anandkumar, Noah D. Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P. Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Gunnar Behrens, Christopher S. Bretherton, Julius J. M. Busecke, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Akshay Subramaniam, Mark A. Taylor, et al.:
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR abs/2306.08754 (2023) - [i229]Hongkai Zheng, Weili Nie, Arash Vahdat, Anima Anandkumar:
Fast Training of Diffusion Models with Masked Transformers. CoRR abs/2306.09305 (2023) - [i228]Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhiming Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer T. Chayes, Hongyu Guo, Jian Tang:
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials. CoRR abs/2306.09375 (2023) - [i227]Jiawei Zhao, Yifei Zhang, Beidi Chen, Florian Schäfer, Anima Anandkumar:
InRank: Incremental Low-Rank Learning. CoRR abs/2306.11250 (2023) - [i226]Kaiyu Yang, Aidan M. Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar:
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models. CoRR abs/2306.15626 (2023) - [i225]Zelun Luo, Yuliang Zou, Yijin Yang, Zane Durante, De-An Huang, Zhiding Yu, Chaowei Xiao, Li Fei-Fei, Animashree Anandkumar:
Differentially Private Video Activity Recognition. CoRR abs/2306.15742 (2023) - [i224]Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i223]Or Sharir, Anima Anandkumar:
Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs. CoRR abs/2307.14988 (2023) - [i222]Colin White, Renbo Tu, Jean Kossaifi, Gennady Pekhimenko, Kamyar Azizzadenesheli, Anima Anandkumar:
Speeding up Fourier Neural Operators via Mixed Precision. CoRR abs/2307.15034 (2023) - [i221]Zhiqi Li, Zhiding Yu, Wenhai Wang, Anima Anandkumar, Tong Lu, José M. Álvarez:
FB-BEV: BEV Representation from Forward-Backward View Transformations. CoRR abs/2308.02236 (2023) - [i220]Yilun Chen, Zhiding Yu, Yukang Chen, Shiyi Lan, Animashree Anandkumar, Jiaya Jia, José M. Álvarez:
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection. CoRR abs/2308.04556 (2023) - [i219]Miguel Liu-Schiaffini, Clare E. Singer, Nikola B. Kovachki, Tapio Schneider, Kamyar Azizzadenesheli, Anima Anandkumar:
Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces. CoRR abs/2308.08794 (2023) - [i218]Zongyi Li, Nikola Borislavov Kovachki, Christopher B. Choy, Boyi Li, Jean Kossaifi, Shourya Prakash Otta, Mohammad Amin Nabian, Maximilian Stadler, Christian Hundt, Kamyar Azizzadenesheli, Anima Anandkumar:
Geometry-Informed Neural Operator for Large-Scale 3D PDEs. CoRR abs/2309.00583 (2023) - [i217]Kamyar Azizzadenesheli, Nikola B. Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, Anima Anandkumar:
Neural Operators for Accelerating Scientific Simulations and Design. CoRR abs/2309.15325 (2023) - [i216]Jean Kossaifi, Nikola B. Kovachki, Kamyar Azizzadenesheli, Anima Anandkumar:
Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs. CoRR abs/2310.00120 (2023) - [i215]Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens:
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR abs/2310.04610 (2023) - [i214]Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, Anima Anandkumar:
Eureka: Human-Level Reward Design via Coding Large Language Models. CoRR abs/2310.12931 (2023) - [i213]Benyamin Allahgholizadeh Haghi, Lin Ma, Sahin Lale, Anima Anandkumar, Azita Emami:
EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification. CoRR abs/2310.15466 (2023) - [i212]Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt Kusner, Marc Peter Deisenroth, Anima Anandkumar, JOREK Team, MAST Team:
Plasma Surrogate Modelling using Fourier Neural Operators. CoRR abs/2311.05967 (2023) - [i211]Rafal Kocielnik, Elyssa Y. Wong, Timothy N. Chu, Lydia Lin, De-An Huang, Jiayun Wang, Anima Anandkumar, Andrew J. Hung:
Deep Multimodal Fusion for Surgical Feedback Classification. CoRR abs/2312.03231 (2023) - [i210]Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson:
Perspectives on the State and Future of Deep Learning - 2023. CoRR abs/2312.09323 (2023) - [i209]Adhithya Prakash Saravanan, Rafal Kocielnik, Roy Jiang, Pengrui Han, Anima Anandkumar:
Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models. CoRR abs/2312.10065 (2023) - 2022
- [j40]Anda Trifan, Defne Gorgun, Michael Salim, Zongyi Li, Alexander Brace, Maxim Zvyagin, Heng Ma, Austin Clyde, David Clark, David J. Hardy, Tom Burnley, Lei Huang, John D. McCalpin, Murali Emani, Hyenseung Yoo, Junqi Yin, Aristeidis Tsaris, Vishal Subbiah, Tanveer Raza, Jessica Liu, Noah Trebesch, Geoffrey Wells, Venkatesh Mysore, Tom Gibbs, James C. Phillips, S. Chakra Chennubhotla, Ian T. Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, John E. Stone, Emad Tajkhorshid, Sarah A. Harris, Arvind Ramanathan:
Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action. Int. J. High Perform. Comput. Appl. 36(5-6): 603-623 (2022) - [j39]Runzhuo Ma, Ashwin Ramaswamy, Jiashu Xu, Loc Trinh, Dani Kiyasseh, Timothy N. Chu, Elyssa Y. Wong, Ryan S. Lee, Ivan Rodriguez, Gina Demeo, Aditya Desai, Maxwell X. Otiato, Sidney I. Roberts, Jessica H. Nguyen, Jasper Laca, Yan Liu, Katarina Urbanova, Christian Wagner, Animashree Anandkumar, Jim C. Hu, Andrew J. Hung:
Surgical gestures as a method to quantify surgical performance and predict patient outcomes. npj Digit. Medicine 5 (2022) - [j38]David Hoeller, Nikita Rudin, Christopher B. Choy, Animashree Anandkumar, Marco Hutter:
Neural Scene Representation for Locomotion on Structured Terrain. IEEE Robotics Autom. Lett. 7(4): 8667-8674 (2022) - [j37]Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung:
Neural-Fly enables rapid learning for agile flight in strong winds. Sci. Robotics 7(66) (2022) - [j36]Jiawei Zhao, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, Mustafa Fayez Ali, Ming-Yu Liu, Brucek Khailany, William J. Dally, Anima Anandkumar:
LNS-Madam: Low-Precision Training in Logarithmic Number System Using Multiplicative Weight Update. IEEE Trans. Computers 71(12): 3179-3190 (2022) - [j35]Md Ashiqur Rahman, Manuel A. Florez, Anima Anandkumar, Zachary E. Ross, Kamyar Azizzadenesheli:
Generative Adversarial Neural Operators. Trans. Mach. Learn. Res. 2022 (2022) - [j34]Jiawei Zhao, Florian Schäfer, Anima Anandkumar:
ZerO Initialization: Initializing Neural Networks with only Zeros and Ones. Trans. Mach. Learn. Res. 2022 (2022) - [c158]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Animashree Anandkumar:
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems. AISTATS 2022: 5354-5390 - [c157]Yuanyuan Shi, Guannan Qu, Steven H. Low, Anima Anandkumar, Adam Wierman:
Stability Constrained Reinforcement Learning for Real-Time Voltage Control. ACC 2022: 2715-2721 - [c156]Yuanyuan Shi, Zongyi Li, Huan Yu, Drew Steeves, Anima Anandkumar, Miroslav Krstic:
Machine Learning Accelerated PDE Backstepping Observers. CDC 2022: 5423-5428 - [c155]Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Animashree Anandkumar, Babak Hassibi:
Thompson Sampling Achieves $\tilde{O}(\sqrt{T})$ Regret in Linear Quadratic Control. COLT 2022: 3235-3284 - [c154]Yulong Cao, Danfei Xu, Xinshuo Weng, Zhuoqing Mao, Anima Anandkumar, Chaowei Xiao, Marco Pavone:
Robust Trajectory Prediction against Adversarial Attacks. CoRL 2022: 128-137 - [c153]Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, José M. Álvarez, Ping Luo, Tong Lu:
Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers. CVPR 2022: 1270-1279 - [c152]Xinlong Wang, Zhiding Yu, Shalini De Mello, Jan Kautz, Anima Anandkumar, Chunhua Shen, José M. Álvarez:
FreeSOLO: Learning to Segment Objects without Annotations. CVPR 2022: 14156-14166 - [c151]Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixé, José M. Álvarez:
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection. CVPR 2022: 14472-14481 - [c150]Huaizu Jiang, Xiaojian Ma, Weili Nie, Zhiding Yu, Yuke Zhu, Anima Anandkumar:
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions. CVPR 2022: 19034-19043 - [c149]Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Mark Kilgard, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing Ren:
Generic lithography modeling with dual-band optics-inspired neural networks. DAC 2022: 973-978 - [c148]Yulong Cao, Chaowei Xiao, Anima Anandkumar, Danfei Xu, Marco Pavone:
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction. ECCV (5) 2022: 36-52 - [c147]Grigorios G. Chrysos, Markos Georgopoulos, Jiankang Deng, Jean Kossaifi, Yannis Panagakis, Anima Anandkumar:
Augmenting Deep Classifiers with Polynomial Neural Networks. ECCV (25) 2022: 692-716 - [c146]John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, Bryan Catanzaro:
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators. ICLR 2022 - [c145]Xiaojian Ma, Weili Nie, Zhiding Yu, Huaizu Jiang, Chaowei Xiao, Yuke Zhu, Song-Chun Zhu, Anima Anandkumar:
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning. ICLR 2022 - [c144]Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Animashree Anandkumar:
Diffusion Models for Adversarial Purification. ICML 2022: 16805-16827 - [c143]Pan Xu, Hongkai Zheng, Eric V. Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar:
Langevin Monte Carlo for Contextual Bandits. ICML 2022: 24830-24850 - [c142]Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, José M. Álvarez:
Understanding The Robustness in Vision Transformers. ICML 2022: 27378-27394 - [c141]Josiah Wong, Viktor Makoviychuk, Anima Anandkumar, Yuke Zhu:
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation. ICRA 2022: 10519-10526 - [c140]Anima Anandkumar:
ScaDL 2022 Invited Talk 3: Million-x speedups through convergence of AI and HPC. IPDPS Workshops 2022: 1041 - [c139]Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar:
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge. NeurIPS 2022 - [c138]De-An Huang, Zhiding Yu, Anima Anandkumar:
MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training. NeurIPS 2022 - [c137]Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Christopher B. Choy, Anima Anandkumar, Minsu Cho, Jaesik Park:
PeRFception: Perception using Radiance Fields. NeurIPS 2022 - [c136]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar:
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. NeurIPS 2022 - [c135]Zongyi Li, Miguel Liu-Schiaffini, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Learning Chaotic Dynamics in Dissipative Systems. NeurIPS 2022 - [c134]Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu:
Pre-Trained Language Models for Interactive Decision-Making. NeurIPS 2022 - [c133]Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao:
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. NeurIPS 2022 - [c132]Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, Bryan Catanzaro:
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models. NeurIPS 2022 - [c131]Bokui Shen, Zhenyu Jiang, Christopher B. Choy, Silvio Savarese, Leonidas J. Guibas, Anima Anandkumar, Yuke Zhu:
ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation. Robotics: Science and Systems 2022 - [c130]Rafal Kocielnik, Sara Kangaslahti, Shrimai Prabhumoye, Meena Hari, R. Michael Alvarez, Anima Anandkumar:
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions. TL4NLP 2022: 22-32 - [d1]Zongyi Li, Miguel Liu-Schiaffini, Nikola Borislavov Kovachki, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Learning Dissipative Dynamics in Chaotic Systems (Datasets). Zenodo, 2022 - [i208]Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu:
Pre-Trained Language Models for Interactive Decision-Making. CoRR abs/2202.01771 (2022) - [i207]Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, Bryan Catanzaro:
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models. CoRR abs/2202.04173 (2022) - [i206]Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik Kashinath, Animashree Anandkumar:
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. CoRR abs/2202.11214 (2022) - [i205]Xinlong Wang, Zhiding Yu, Shalini De Mello, Jan Kautz, Anima Anandkumar, Chunhua Shen, José M. Álvarez:
FreeSOLO: Learning to Segment Objects without Annotations. CoRR abs/2202.12181 (2022) - [i204]Bokui Shen, Zhenyu Jiang, Christopher B. Choy, Leonidas J. Guibas, Silvio Savarese, Anima Anandkumar, Yuke Zhu:
ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation. CoRR abs/2203.06856 (2022) - [i203]Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Mark Kilgard, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing Ren:
Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks. CoRR abs/2203.08616 (2022) - [i202]Enze Xie, Zhiding Yu, Daquan Zhou, Jonah Philion, Anima Anandkumar, Sanja Fidler, Ping Luo, José M. Álvarez:
M2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation. CoRR abs/2204.05088 (2022) - [i201]Xiaojian Ma, Weili Nie, Zhiding Yu, Huaizu Jiang, Chaowei Xiao, Yuke Zhu, Song-Chun Zhu, Anima Anandkumar:
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning. CoRR abs/2204.11167 (2022) - [i200]Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng, José M. Álvarez:
Understanding The Robustness in Vision Transformers. CoRR abs/2204.12451 (2022) - [i199]Md Ashiqur Rahman, Manuel A. Florez, Anima Anandkumar, Zachary E. Ross, Kamyar Azizzadenesheli:
Generative Adversarial Neural Operators. CoRR abs/2205.03017 (2022) - [i198]Dani Kiyasseh, Runzhuo Ma, Taseen F. Haque, Jessica H. Nguyen, Christian Wagner, Animashree Anandkumar, Andrew J. Hung:
Quantification of Robotic Surgeries with Vision-Based Deep Learning. CoRR abs/2205.03028 (2022) - [i197]Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung:
Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds. CoRR abs/2205.06908 (2022) - [i196]Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar:
Diffusion Models for Adversarial Purification. CoRR abs/2205.07460 (2022) - [i195]Huaizu Jiang, Xiaojian Ma, Weili Nie, Zhiding Yu, Yuke Zhu, Anima Anandkumar:
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions. CoRR abs/2205.13803 (2022) - [i194]Sahin Lale, Yuanyuan Shi, Guannan Qu, Kamyar Azizzadenesheli, Adam Wierman, Anima Anandkumar:
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems. CoRR abs/2206.01704 (2022) - [i193]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar:
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. CoRR abs/2206.03520 (2022) - [i192]David Hoeller, Nikita Rudin, Christopher B. Choy, Animashree Anandkumar, Marco Hutter:
Neural Scene Representation for Locomotion on Structured Terrain. CoRR abs/2206.08077 (2022) - [i191]Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi:
Thompson Sampling Achieves Õ(√T) Regret in Linear Quadratic Control. CoRR abs/2206.08520 (2022) - [i190]Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar:
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge. CoRR abs/2206.08853 (2022) - [i189]Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar:
Langevin Monte Carlo for Contextual Bandits. CoRR abs/2206.11254 (2022) - [i188]Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing Ren:
Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training. CoRR abs/2207.04056 (2022) - [i187]Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar:
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries. CoRR abs/2207.05209 (2022) - [i186]Yulong Cao, Danfei Xu, Xinshuo Weng, Zhuoqing Mao, Anima Anandkumar, Chaowei Xiao, Marco Pavone:
Robust Trajectory Prediction against Adversarial Attacks. CoRR abs/2208.00094 (2022) - [i185]De-An Huang, Zhiding Yu, Anima Anandkumar:
MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training. CoRR abs/2208.02245 (2022) - [i184]Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Animashree Anandkumar:
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators. CoRR abs/2208.05419 (2022) - [i183]Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard G. Baraniuk, Anima Anandkumar:
Retrieval-based Controllable Molecule Generation. CoRR abs/2208.11126 (2022) - [i182]Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Christopher B. Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park:
PeRFception: Perception using Radiance Fields. CoRR abs/2208.11537 (2022) - [i181]Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao:
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. CoRR abs/2209.07511 (2022) - [i180]Jie Feng, Yuanyuan Shi, Guannan Qu, Steven H. Low, Anima Anandkumar, Adam Wierman:
Stability Constrained Reinforcement Learning for Real-Time Voltage Control in Distribution Systems. CoRR abs/2209.07669 (2022) - [i179]Yulong Cao, Chaowei Xiao, Anima Anandkumar, Danfei Xu, Marco Pavone:
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction. CoRR abs/2209.08744 (2022) - [i178]Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Anima Anandkumar:
Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models. CoRR abs/2209.15171 (2022) - [i177]Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, Linxi Fan:
VIMA: General Robot Manipulation with Multimodal Prompts. CoRR abs/2210.03094 (2022) - [i176]Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro:
Context Generation Improves Open Domain Question Answering. CoRR abs/2210.06349 (2022) - [i175]Junfei Xiao, Zhichao Xu, Shiyi Lan, Zhiding Yu, Alan L. Yuille, Anima Anandkumar:
1st Place Solution of The Robust Vision Challenge (RVC) 2022 Semantic Segmentation Track. CoRR abs/2210.12852 (2022) - [i174]Mingjie Liu, Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Selim Dogru, Anima Anandkumar, David Z. Pan, Brucek Khailany, Haoxing Ren:
An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design. CoRR abs/2210.15765 (2022) - [i173]Gege Wen, Zongyi Li, Qirui Long, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson:
Accelerating Carbon Capture and Storage Modeling using Fourier Neural Operators. CoRR abs/2210.17051 (2022) - [i172]Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song:
DensePure: Understanding Diffusion Models towards Adversarial Robustness. CoRR abs/2211.00322 (2022) - [i171]Rafal Kocielnik, Sara Kangaslahti, Shrimai Prabhumoye, Meena Hari, R. Michael Alvarez, Anima Anandkumar:
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions. CoRR abs/2211.11798 (2022) - [i170]Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, Anima Anandkumar:
Fast Sampling of Diffusion Models via Operator Learning. CoRR abs/2211.13449 (2022) - [i169]Yuanyuan Shi, Zongyi Li, Huan Yu, Drew Steeves, Anima Anandkumar, Miroslav Krstic:
Machine Learning Accelerated PDE Backstepping Observers. CoRR abs/2211.15044 (2022) - [i168]Jiawei Zhao, Robert Joseph George, Yifei Zhang, Zongyi Li, Anima Anandkumar:
Incremental Fourier Neural Operator. CoRR abs/2211.15188 (2022) - [i167]Haydn Maust, Zongyi Li, Yixuan Wang, Daniel V. Leibovici, Oscar P. Bruno, Thomas Y. Hou, Anima Anandkumar:
Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators. CoRR abs/2211.15960 (2022) - [i166]Jiaqi Gu, Ben Keller, Jean Kossaifi, Anima Anandkumar, Brucek Khailany, David Z. Pan:
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression. CoRR abs/2211.16749 (2022) - [i165]Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Anima Anandkumar:
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing. CoRR abs/2212.10789 (2022) - [i164]Or Sharir, Garnet Kin-Lic Chan, Anima Anandkumar:
Towards Neural Variational Monte Carlo That Scales Linearly with System Size. CoRR abs/2212.11296 (2022) - 2021
- [j33]Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Adrian Bulat, Animashree Anandkumar, Ioanna Tzoulaki, Paul M. Matthews:
Tensor Dropout for Robust Learning. IEEE J. Sel. Top. Signal Process. 15(3): 630-640 (2021) - [j32]Yannis Panagakis, Jean Kossaifi, Grigorios G. Chrysos, James Oldfield, Mihalis A. Nicolaou, Anima Anandkumar, Stefanos Zafeiriou:
Tensor Methods in Computer Vision and Deep Learning. Proc. IEEE 109(5): 863-890 (2021) - [j31]Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung:
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems. IEEE Robotics Autom. Lett. 6(1): 389-396 (2021) - [c129]Ravi Tej Akella, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Animashree Anandkumar, Yisong Yue:
Deep Bayesian Quadrature Policy Optimization. AAAI 2021: 6600-6608 - [c128]Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue:
Active Learning under Label Shift. AISTATS 2021: 3412-3420 - [c127]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting. ACC 2021: 2517-2522 - [c126]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Model Learning Predictive Control in Nonlinear Dynamical Systems. CDC 2021: 757-762 - [c125]Youngwoon Lee, Joseph J. Lim, Anima Anandkumar, Yuke Zhu:
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization. CoRL 2021: 406-416 - [c124]Shiyi Lan, Zhiding Yu, Christopher B. Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry S. Davis, Anima Anandkumar:
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision. ICCV 2021: 3386-3396 - [c123]Yoonwoo Jeong, Seokjun Ahn, Christopher B. Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park:
Self-Calibrating Neural Radiance Fields. ICCV 2021: 5826-5834 - [c122]Wuyang Chen, Zhiding Yu, Shalini De Mello, Sifei Liu, José M. Álvarez, Zhangyang Wang, Anima Anandkumar:
Contrastive Syn-to-Real Generalization. ICLR 2021 - [c121]Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Fourier Neural Operator for Parametric Partial Differential Equations. ICLR 2021 - [c120]Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Animashree Anandkumar, Sanja Fidler, José M. Álvarez:
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection. ICML 2021: 1463-1472 - [c119]Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Animashree Anandkumar:
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies. ICML 2021: 3088-3099 - [c118]Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar:
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. ICML 2021: 6860-6870 - [c117]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. ICML 2021: 7301-7312 - [c116]Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Animashree Anandkumar, Yuke Zhu:
Fast Uncertainty Quantification for Deep Object Pose Estimation. ICRA 2021: 5200-5207 - [c115]Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu:
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects. ICRA 2021: 7540-7547 - [c114]Grigorios G. Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar:
Unsupervised Controllable Generation with Self-Training. IJCNN 2021: 1-8 - [c113]Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio, Timothy Tsai, Alejandro J. Troccoli, Stephen W. Keckler, Siddharth Garg, Anima Anandkumar:
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles. IV 2021: 157-164 - [c112]Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael Alvarez, Anima Anandkumar:
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions. KDD 2021: 3576-3584 - [c111]Sahin Lale, Oguzhan Teke, Babak Hassibi, Anima Anandkumar:
Stability and Identification of Random Asynchronous Linear Time-Invariant Systems. L4DC 2021: 651-663 - [c110]Guannan Qu, Yuanyuan Shi, Sahin Lale, Anima Anandkumar, Adam Wierman:
Stable Online Control of Linear Time-Varying Systems. L4DC 2021: 742-753 - [c109]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems. L4DC 2021: 967-979 - [c108]Jing Yu, Clement Gehring, Florian Schäfer, Animashree Anandkumar:
Robust Reinforcement Learning: A Constrained Game-theoretic Approach. L4DC 2021: 1242-1254 - [c107]Aishan Liu, Xinyun Chen, Yingwei Li, Chaowei Xiao, Xun Yang, Xianglong Liu, Dawn Song, Dacheng Tao, Alan L. Yuille, Anima Anandkumar:
ADVM'21: 1st International Workshop on Adversarial Learning for Multimedia. ACM Multimedia 2021: 5686-5687 - [c106]Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang:
AugMax: Adversarial Composition of Random Augmentations for Robust Training. NeurIPS 2021: 237-250 - [c105]Zhiding Yu, Rui Huang, Wonmin Byeon, Sifei Liu, Guilin Liu, Thomas M. Breuel, Anima Anandkumar, Jan Kautz:
Coupled Segmentation and Edge Learning via Dynamic Graph Propagation. NeurIPS 2021: 4919-4932 - [c104]Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, José M. Álvarez, Ping Luo:
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. NeurIPS 2021: 12077-12090 - [c103]Weili Nie, Arash Vahdat, Anima Anandkumar:
Controllable and Compositional Generation with Latent-Space Energy-Based Models. NeurIPS 2021: 13497-13510 - [c102]Jiachen Sun, Yulong Cao, Christopher B. Choy, Zhiding Yu, Anima Anandkumar, Zhuoqing Morley Mao, Chaowei Xiao:
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions. NeurIPS 2021: 15498-15512 - [c101]Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro:
Long-Short Transformer: Efficient Transformers for Language and Vision. NeurIPS 2021: 17723-17736 - [c100]Yujia Huang, Huan Zhang, Yuanyuan Shi, J. Zico Kolter, Anima Anandkumar:
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds. NeurIPS 2021: 22745-22757 - [c99]Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar:
Competitive policy optimization. UAI 2021: 64-74 - [e1]Dawn Song, Dacheng Tao, Alan L. Yuille, Anima Anandkumar, Aishan Liu, Xinyun Chen, Yingwei Li, Chaowei Xiao, Xun Yang, Xianglong Liu:
ADVM '21: Proceedings of the 1st International Workshop on Adversarial Learning for Multimedia, Virtual Event, China, 20 October 2021. ACM 2021, ISBN 978-1-4503-8672-2 [contents] - [i163]Anqi Liu, Hao Liu, Tongxin Li, Saeed Karimi-Bidhendi, Yisong Yue, Anima Anandkumar:
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments. CoRR abs/2101.06614 (2021) - [i162]Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael Alvarez, Anima Anandkumar:
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions. CoRR abs/2102.12596 (2021) - [i161]Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio, Timothy Tsai, Alejandro J. Troccoli, Stephen W. Keckler, Siddharth Garg, Anima Anandkumar:
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles. CoRR abs/2103.07403 (2021) - [i160]Wuyang Chen, Zhiding Yu, Shalini De Mello, Sifei Liu, José M. Álvarez, Zhangyang Wang, Anima Anandkumar:
Contrastive Syn-to-Real Generalization. CoRR abs/2104.02290 (2021) - [i159]Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, José M. Álvarez:
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection. CoRR abs/2104.05702 (2021) - [i158]Guannan Qu, Yuanyuan Shi, Sahin Lale, Anima Anandkumar, Adam Wierman:
Stable Online Control of Linear Time-Varying Systems. CoRR abs/2104.14134 (2021) - [i157]Shiyi Lan, Zhiding Yu, Christopher Bongsoo Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry S. Davis, Anima Anandkumar:
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision. CoRR abs/2105.06464 (2021) - [i156]Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar:
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition. CoRR abs/2105.08692 (2021) - [i155]Zhuoran Qiao, Anders S. Christensen, Frederick R. Manby, Matthew Welborn, Anima Anandkumar, Thomas F. Miller III:
UNiTE: Unitary N-body Tensor Equivariant Network with Applications to Quantum Chemistry. CoRR abs/2105.14655 (2021) - [i154]Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, José M. Álvarez, Ping Luo:
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. CoRR abs/2105.15203 (2021) - [i153]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. CoRR abs/2106.00136 (2021) - [i152]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Markov Neural Operators for Learning Chaotic Systems. CoRR abs/2106.06898 (2021) - [i151]Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar:
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies. CoRR abs/2106.09678 (2021) - [i150]Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixé, José M. Álvarez:
Towards Reducing Labeling Cost in Deep Object Detection. CoRR abs/2106.11921 (2021) - [i149]Jiawei Zhao, Steve Dai, Rangharajan Venkatesan, Ming-Yu Liu, Brucek Khailany, Bill Dally, Anima Anandkumar:
Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update. CoRR abs/2106.13914 (2021) - [i148]Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro:
Long-Short Transformer: Efficient Transformers for Language and Vision. CoRR abs/2107.02192 (2021) - [i147]Yannis Panagakis, Jean Kossaifi, Grigorios G. Chrysos, James Oldfield, Mihalis A. Nicolaou, Anima Anandkumar, Stefanos Zafeiriou:
Tensor Methods in Computer Vision and Deep Learning. CoRR abs/2107.03436 (2021) - [i146]Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Learning Maps Between Function Spaces. CoRR abs/2108.08481 (2021) - [i145]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems. CoRR abs/2108.11959 (2021) - [i144]Yoonwoo Jeong, Seokjun Ahn, Christopher B. Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park:
Self-Calibrating Neural Radiance Fields. CoRR abs/2108.13826 (2021) - [i143]Gege Wen, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson:
U-FNO - an enhanced Fourier neural operator based-deep learning model for multiphase flow. CoRR abs/2109.03697 (2021) - [i142]Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, José M. Álvarez, Tong Lu, Ping Luo:
Panoptic SegFormer. CoRR abs/2109.03814 (2021) - [i141]Homanga Bharadhwaj, De-An Huang, Chaowei Xiao, Anima Anandkumar, Animesh Garg:
Auditing AI models for Verified Deployment under Semantic Specifications. CoRR abs/2109.12456 (2021) - [i140]Yuanyuan Shi, Guannan Qu, Steven H. Low, Anima Anandkumar, Adam Wierman:
Stability Constrained Reinforcement Learning for Real-Time Voltage Control. CoRR abs/2109.14854 (2021) - [i139]Josiah Wong, Viktor Makoviychuk, Anima Anandkumar, Yuke Zhu:
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation. CoRR abs/2110.00704 (2021) - [i138]Weili Nie, Arash Vahdat, Anima Anandkumar:
Controllable and Compositional Generation with Latent-Space Energy-Based Models. CoRR abs/2110.10873 (2021) - [i137]Jiawei Zhao, Florian Schäfer, Anima Anandkumar:
ZerO Initialization: Initializing Residual Networks with only Zeros and Ones. CoRR abs/2110.12661 (2021) - [i136]Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang:
AugMax: Adversarial Composition of Random Augmentations for Robust Training. CoRR abs/2110.13771 (2021) - [i135]Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar:
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions. CoRR abs/2110.14538 (2021) - [i134]Yujia Huang, Huan Zhang, Yuanyuan Shi, J. Zico Kolter, Anima Anandkumar:
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds. CoRR abs/2111.01395 (2021) - [i133]Zongyi Li, Hongkai Zheng, Nikola B. Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, Anima Anandkumar:
Physics-Informed Neural Operator for Learning Partial Differential Equations. CoRR abs/2111.03794 (2021) - [i132]Youngwoon Lee, Joseph J. Lim, Anima Anandkumar, Yuke Zhu:
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization. CoRR abs/2111.07999 (2021) - [i131]Jeffrey Ma, Alistair Letcher, Florian Schäfer, Yuanyuan Shi, Anima Anandkumar:
Polymatrix Competitive Gradient Descent. CoRR abs/2111.08565 (2021) - [i130]John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, Bryan Catanzaro:
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers. CoRR abs/2111.13587 (2021) - [i129]Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atilim Günes Baydin, Carina Prunkl, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer:
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR abs/2112.03235 (2021) - [i128]Kevin Huang, Sahin Lale, Ugo Rosolia, Yuanyuan Shi, Anima Anandkumar:
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning. CoRR abs/2112.07746 (2021) - [i127]Shrimai Prabhumoye, Rafal Kocielnik, Mohammad Shoeybi, Anima Anandkumar, Bryan Catanzaro:
Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases. CoRR abs/2112.07868 (2021) - 2020
- [j30]Jean Kossaifi, Zachary C. Lipton, Arinbjörn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar:
Tensor Regression Networks. J. Mach. Learn. Res. 21: 123:1-123:21 (2020) - [c98]Francesca Baldini, Animashree Anandkumar, Richard M. Murray:
Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data. ACC 2020: 2961-2966 - [c97]Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Anima Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg:
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. CoRL 2020: 883-894 - [c96]Yang Shi, Animashree Anandkumar:
Higher-Order Count Sketch: Dimensionality Reduction that Retains Efficient Tensor Operations. DCC 2020: 394 - [c95]Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro:
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models. EMNLP (1) 2020: 2831-2845 - [c94]Animashree Anandkumar:
Role of HPC in next-generation AI. HiPC 2020: xx - [c93]Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar:
Angular Visual Hardness. ICML 2020: 1637-1648 - [c92]Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar:
Automated Synthetic-to-Real Generalization. ICML 2020: 1746-1756 - [c91]Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Animashree Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. ICML 2020: 7360-7369 - [c90]Florian Schäfer, Hongkai Zheng, Animashree Anandkumar:
Implicit competitive regularization in GANs. ICML 2020: 8533-8544 - [c89]Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue:
Robust Regression for Safe Exploration in Control. L4DC 2020: 608-619 - [c88]Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue:
Learning compositional functions via multiplicative weight updates. NeurIPS 2020 - [c87]Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. NeurIPS 2020 - [c86]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems. NeurIPS 2020 - [c85]Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. NeurIPS 2020 - [c84]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar:
Multipole Graph Neural Operator for Parametric Partial Differential Equations. NeurIPS 2020 - [c83]Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar:
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning. NeurIPS 2020 - [c82]Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar:
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning. NeurIPS 2020 - [c81]Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar:
MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. SC 2020: 9 - [c80]Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. UAI 2020: 1378-1387 - [i126]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Regret Minimization in Partially Observable Linear Quadratic Control. CoRR abs/2002.00082 (2020) - [i125]Jiahao Su, Wonmin Byeon, Furong Huang, Jan Kautz, Animashree Anandkumar:
Convolutional Tensor-Train LSTM for Spatio-temporal Learning. CoRR abs/2002.09131 (2020) - [i124]Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Anima Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. CoRR abs/2003.03461 (2020) - [i123]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Graph Kernel Network for Partial Differential Equations. CoRR abs/2003.03485 (2020) - [i122]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems. CoRR abs/2003.05999 (2020) - [i121]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems. CoRR abs/2003.11227 (2020) - [i120]Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar:
Spectral Learning on Matrices and Tensors. CoRR abs/2004.07984 (2020) - [i119]Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar:
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. CoRR abs/2005.01463 (2020) - [i118]Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung:
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems. CoRR abs/2005.04374 (2020) - [i117]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Multipole Graph Neural Operator for Parametric Partial Differential Equations. CoRR abs/2006.09535 (2020) - [i116]Florian Schäfer, Anima Anandkumar, Houman Owhadi:
Competitive Mirror Descent. CoRR abs/2006.10179 (2020) - [i115]Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar:
Competitive Policy Optimization. CoRR abs/2006.10611 (2020) - [i114]Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue:
Learning compositional functions via multiplicative weight updates. CoRR abs/2006.14560 (2020) - [i113]Ravi Tej Akella, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue:
Deep Bayesian Quadrature Policy Optimization. CoRR abs/2006.15637 (2020) - [i112]Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. CoRR abs/2007.00631 (2020) - [i111]Wuyang Chen, Zhiding Yu, Zhangyang Wang, Anima Anandkumar:
Automated Synthetic-to-Real Generalization. CoRR abs/2007.06965 (2020) - [i110]Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III:
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features. CoRR abs/2007.08026 (2020) - [i109]Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue:
Active Learning under Label Shift. CoRR abs/2007.08479 (2020) - [i108]Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. CoRR abs/2007.09200 (2020) - [i107]Grigorios G. Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar:
Unsupervised Controllable Generation with Self-Training. CoRR abs/2007.09250 (2020) - [i106]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Explore More and Improve Regret in Linear Quadratic Regulators. CoRR abs/2007.12291 (2020) - [i105]Hongyu Ren, Yuke Zhu, Jure Leskovec, Anima Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. CoRR abs/2008.07087 (2020) - [i104]Francisco Luongo, Ryan Hakim, Jessica H. Nguyen, Animashree Anandkumar, Andrew J. Hung:
Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery. CoRR abs/2008.11833 (2020) - [i103]Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg:
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. CoRR abs/2009.10019 (2020) - [i102]Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Animashree Anandkumar:
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning. CoRR abs/2010.00763 (2020) - [i101]Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro:
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models. CoRR abs/2010.00840 (2020) - [i100]Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, Anima Anandkumar:
Distributionally Robust Learning for Unsupervised Domain Adaptation. CoRR abs/2010.05784 (2020) - [i99]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Fourier Neural Operator for Parametric Partial Differential Equations. CoRR abs/2010.08895 (2020) - [i98]Zhuoran Qiao, Feizhi Ding, Matthew Welborn, Peter J. Bygrave, Daniel G. A. Smith, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III:
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces. CoRR abs/2011.02680 (2020) - [i97]Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Animashree Anandkumar, Yuke Zhu:
Fast Uncertainty Quantification for Deep Object Pose Estimation. CoRR abs/2011.07748 (2020) - [i96]Sahin Lale, Oguzhan Teke, Babak Hassibi, Anima Anandkumar:
Stability and Identification of Random Asynchronous Linear Time-Invariant Systems. CoRR abs/2012.04160 (2020) - [i95]Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu:
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects. CoRR abs/2012.12209 (2020)
2010 – 2019
- 2019
- [j29]Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar:
Spectral Learning on Matrices and Tensors. Found. Trends Mach. Learn. 12(5-6): 393-536 (2019) - [j28]Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic:
TensorLy: Tensor Learning in Python. J. Mach. Learn. Res. 20: 26:1-26:6 (2019) - [c79]Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar:
Regularized Learning for Domain Adaptation under Label Shifts. ICLR (Poster) 2019 - [c78]Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD with Majority Vote is Communication Efficient and Fault Tolerant. ICLR (Poster) 2019 - [c77]Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan:
Active Learning with Partial Feedback. ICLR (Poster) 2019 - [c76]Milan Cvitkovic, Badal Singh, Animashree Anandkumar:
Open Vocabulary Learning on Source Code with a Graph-Structured Cache. ICML 2019: 1475-1485 - [c75]Shobhit Jain, Sravan Babu Bodapati, Ramesh Nallapati, Anima Anandkumar:
Multi Sense Embeddings from Topic Models. ICNLSP 2019: 34-41 - [c74]Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung:
Neural Lander: Stable Drone Landing Control Using Learned Dynamics. ICRA 2019: 9784-9790 - [c73]Florian Schäfer, Anima Anandkumar:
Competitive Gradient Descent. NeurIPS 2019: 7623-7633 - [c72]Furong Huang, U. N. Niranjan, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar:
Guaranteed Scalable Learning of Latent Tree Models. UAI 2019: 883-893 - [i94]Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi:
Stochastic Linear Bandits with Hidden Low Rank Structure. CoRR abs/1901.09490 (2019) - [i93]Yang Shi, Animashree Anandkumar:
Multi-dimensional Tensor Sketch. CoRR abs/1901.11261 (2019) - [i92]Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Ioanna Tzoulaki, Paul M. Matthews:
Stochastically Rank-Regularized Tensor Regression Networks. CoRR abs/1902.10758 (2019) - [i91]Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar:
Regularized Learning for Domain Adaptation under Label Shifts. CoRR abs/1903.09734 (2019) - [i90]Florian Schäfer, Anima Anandkumar:
Competitive Gradient Descent. CoRR abs/1905.12103 (2019) - [i89]Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue:
Robust Regression for Safe Exploration in Control. CoRR abs/1906.05819 (2019) - [i88]Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello:
Learning Causal State Representations of Partially Observable Environments. CoRR abs/1906.10437 (2019) - [i87]Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli, Anima Anandkumar:
Directivity Modes of Earthquake Populations with Unsupervised Learning. CoRR abs/1907.00496 (2019) - [i86]Yujia Huang, Sihui Dai, Tan M. Nguyen, Richard G. Baraniuk, Anima Anandkumar:
Out-of-Distribution Detection Using Neural Rendering Generative Models. CoRR abs/1907.04572 (2019) - [i85]Shobhit Jain, Sravan Babu Bodapati, Ramesh Nallapati, Anima Anandkumar:
Multi Sense Embeddings from Topic Models. CoRR abs/1909.07746 (2019) - [i84]Florian Schäfer, Hongkai Zheng, Anima Anandkumar:
Implicit competitive regularization in GANs. CoRR abs/1910.05852 (2019) - [i83]Forough Arabshahi, Zhichu Lu, Sameer Singh, Animashree Anandkumar:
Memory Augmented Recursive Neural Networks. CoRR abs/1911.01545 (2019) - [i82]Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen, R. Michael Alvarez, Anima Anandkumar:
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates. CoRR abs/1911.05332 (2019) - [i81]Anqi Liu, Hao Liu, Anima Anandkumar, Yisong Yue:
Triply Robust Off-Policy Evaluation. CoRR abs/1911.05811 (2019) - [i80]Beidi Chen, Weiyang Liu, Animesh Garg, Zhiding Yu, Anshumali Shrivastava, Jan Kautz, Anima Anandkumar:
Angular Visual Hardness. CoRR abs/1912.02279 (2019) - [i79]Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar:
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers. CoRR abs/1912.03978 (2019) - [i78]Francesca Baldini, Animashree Anandkumar, Richard M. Murray:
Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data. CoRR abs/1912.04527 (2019) - 2018
- [j27]Evrim Acar, Animashree Anandkumar, Lenore Mullin, Sebnem Rusitschka, Volker Tresp:
Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152). Dagstuhl Manifestos 7(1): 52-68 (2018) - [c71]Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar:
Probabilistic FastText for Multi-Sense Word Embeddings. ACL (1) 2018: 1-11 - [c70]Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar:
Question Type Guided Attention in Visual Question Answering. ECCV (4) 2018: 158-175 - [c69]Forough Arabshahi, Sameer Singh, Animashree Anandkumar:
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs. ICLR (Poster) 2018 - [c68]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
Compression by the signs: distributed learning is a two-way street. ICLR (Workshop) 2018 - [c67]Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Animashree Anandkumar:
Stochastic Activation Pruning for Robust Adversarial Defense. ICLR (Poster) 2018 - [c66]Ashish Khetan, Zachary C. Lipton, Animashree Anandkumar:
Learning From Noisy Singly-labeled Data. ICLR (Poster) 2018 - [c65]Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. ICLR (Poster) 2018 - [c64]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar:
SIGNSGD: Compressed Optimisation for Non-Convex Problems. ICML 2018: 559-568 - [c63]Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar:
Born-Again Neural Networks. ICML 2018: 1602-1611 - [c62]Michael Tschannen, Aran Khanna, Animashree Anandkumar:
StrassenNets: Deep Learning with a Multiplication Budget. ICML 2018: 4992-5001 - [c61]Kamyar Azizzadenesheli, Emma Brunskill, Animashree Anandkumar:
Efficient Exploration Through Bayesian Deep Q-Networks. ITA 2018: 1-9 - [i77]Forough Arabshahi, Sameer Singh, Animashree Anandkumar:
Combining Symbolic and Function Evaluation Expressions In Neural Programs. CoRR abs/1801.04342 (2018) - [i76]Kamyar Azizzadenesheli, Emma Brunskill, Animashree Anandkumar:
Efficient Exploration through Bayesian Deep Q-Networks. CoRR abs/1802.04412 (2018) - [i75]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD: compressed optimisation for non-convex problems. CoRR abs/1802.04434 (2018) - [i74]Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan:
Active Learning with Partial Feedback. CoRR abs/1802.07427 (2018) - [i73]Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar:
Stochastic Activation Pruning for Robust Adversarial Defense. CoRR abs/1803.01442 (2018) - [i72]Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar:
Question Type Guided Attention in Visual Question Answering. CoRR abs/1804.02088 (2018) - [i71]Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar:
Born Again Neural Networks. CoRR abs/1805.04770 (2018) - [i70]Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar:
Probabilistic FastText for Multi-Sense Word Embeddings. CoRR abs/1806.02901 (2018) - [i69]Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Emma Brunskill, Zachary C. Lipton, Animashree Anandkumar:
Sample-Efficient Deep RL with Generative Adversarial Tree Search. CoRR abs/1806.05780 (2018) - [i68]Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant. CoRR abs/1810.05291 (2018) - [i67]Kamyar Azizzadenesheli, Manish Kumar Bera, Animashree Anandkumar:
Trust Region Policy Optimization of POMDPs. CoRR abs/1810.07900 (2018) - [i66]Milan Cvitkovic, Badal Singh, Anima Anandkumar:
Open Vocabulary Learning on Source Code with a Graph-Structured Cache. CoRR abs/1810.08305 (2018) - [i65]Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk:
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. CoRR abs/1811.02657 (2018) - [i64]Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung:
Neural Lander: Stable Drone Landing Control using Learned Dynamics. CoRR abs/1811.08027 (2018) - 2017
- [j26]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Analyzing Tensor Power Method Dynamics in Overcomplete Regime. J. Mach. Learn. Res. 18: 22:1-22:40 (2017) - [j25]Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli:
A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries. IEEE Trans. Inf. Theory 63(1): 575-592 (2017) - [c60]Forough Arabshahi, Anima Anandkumar:
Spectral Methods for Correlated Topic Models. AISTATS 2017: 1439-1447 - [c59]Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi:
Homotopy Analysis for Tensor PCA. COLT 2017: 79-104 - [c58]Jean Kossaifi, Aran Khanna, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar:
Tensor Contraction Layers for Parsimonious Deep Nets. CVPR Workshops 2017: 1940-1946 - [c57]Yanyao Shen, Hyokun Yun, Zachary Chase Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. Rep4NLP@ACL 2017: 252-256 - [i63]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods. CoRR abs/1705.02553 (2017) - [i62]Jean Kossaifi, Aran Khanna, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar:
Tensor Contraction Layers for Parsimonious Deep Nets. CoRR abs/1706.00439 (2017) - [i61]Yang Shi, Tommaso Furlanello, Anima Anandkumar:
Compact Tensor Pooling for Visual Question Answering. CoRR abs/1706.06706 (2017) - [i60]Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. CoRR abs/1707.05928 (2017) - [i59]Jean Kossaifi, Zachary C. Lipton, Aran Khanna, Tommaso Furlanello, Anima Anandkumar:
Tensor Regression Networks. CoRR abs/1707.08308 (2017) - [i58]Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue:
Long-term Forecasting using Tensor-Train RNNs. CoRR abs/1711.00073 (2017) - [i57]Michael Tschannen, Aran Khanna, Anima Anandkumar:
StrassenNets: Deep learning with a multiplication budget. CoRR abs/1712.03942 (2017) - [i56]Ashish Khetan, Zachary C. Lipton, Anima Anandkumar:
Learning From Noisy Singly-labeled Data. CoRR abs/1712.04577 (2017) - 2016
- [j24]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli:
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. 26(4): 2775-2799 (2016) - [c56]Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan:
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. AISTATS 2016: 268-276 - [c55]Hanie Sedghi, Majid Janzamin, Anima Anandkumar:
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models. AISTATS 2016: 1223-1231 - [c54]Animashree Anandkumar, Rong Ge:
Efficient approaches for escaping higher order saddle points in non-convex optimization. COLT 2016: 81-102 - [c53]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Reinforcement Learning of POMDPs using Spectral Methods. COLT 2016: 193-256 - [c52]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies. COLT 2016: 1639-1642 - [c51]Yang Shi, U. N. Niranjan, Animashree Anandkumar, Cris Cecka:
Tensor Contractions with Extended BLAS Kernels on CPU and GPU. HiPC 2016: 193-202 - [c50]Yining Wang, Anima Anandkumar:
Online and Differentially-Private Tensor Decomposition. NIPS 2016: 3531-3539 - [i55]Anima Anandkumar, Rong Ge:
Efficient approaches for escaping higher order saddle points in non-convex optimization. CoRR abs/1602.05908 (2016) - [i54]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Reinforcement Learning of POMDP's using Spectral Methods. CoRR abs/1602.07764 (2016) - [i53]Hanie Sedghi, Anima Anandkumar:
Training Input-Output Recurrent Neural Networks through Spectral Methods. CoRR abs/1603.00954 (2016) - [i52]Forough Arabshahi, Animashree Anandkumar:
Beyond LDA: A Unified Framework for Learning Latent Normalized Infinitely Divisible Topic Models through Spectral Methods. CoRR abs/1605.09080 (2016) - [i51]Yang Shi, U. N. Niranjan, Animashree Anandkumar, Cris Cecka:
Tensor Contractions with Extended BLAS Kernels on CPU and GPU. CoRR abs/1606.05696 (2016) - [i50]Yining Wang, Animashree Anandkumar:
Online and Differentially-Private Tensor Decomposition. CoRR abs/1606.06237 (2016) - [i49]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies. CoRR abs/1608.04996 (2016) - [i48]Anthony Gitter, Furong Huang, Ragupathyraj Valluvan, Ernest Fraenkel, Animashree Anandkumar:
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models. CoRR abs/1609.06335 (2016) - [i47]Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi:
Homotopy Method for Tensor Principal Component Analysis. CoRR abs/1610.09322 (2016) - [i46]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Reinforcement Learning of Contextual MDPs using Spectral Methods. CoRR abs/1611.03907 (2016) - [i45]Evrim Acar, Animashree Anandkumar, Lenore Mullin, Sebnem Rusitschka, Volker Tresp:
Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152). Dagstuhl Reports 6(4): 57-79 (2016) - 2015
- [j23]Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu:
A Spectral Algorithm for Latent Dirichlet Allocation. Algorithmica 72(1): 193-214 (2015) - [j22]Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade:
When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity. J. Mach. Learn. Res. 16: 2643-2694 (2015) - [j21]Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Animashree Anandkumar:
Online tensor methods for learning latent variable models. J. Mach. Learn. Res. 16: 2797-2835 (2015) - [c49]Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). ALT 2015: 19-38 - [c48]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Learning Overcomplete Latent Variable Models through Tensor Methods. COLT 2015: 36-112 - [c47]Forough Arabshahi, Furong Huang, Animashree Anandkumar, Carter T. Butts, Sean M. Fitzhugh:
Are You Going to the Party: Depends, Who Else is Coming?: [Learning Hidden Group Dynamics via Conditional Latent Tree Models]. ICDM 2015: 697-702 - [c46]Furong Huang, Animashree Anandkumar:
Convolutional Dictionary Learning through Tensor Factorization. FE@NIPS 2015: 116-129 - [c45]Majid Janzamin, Hanie Sedghi, U. N. Niranjan, Animashree Anandkumar:
FEAST at Play: Feature ExtrAction using Score function Tensors. FE@NIPS 2015: 130-144 - [c44]Yining Wang, Hsiao-Yu Fish Tung, Alexander J. Smola, Anima Anandkumar:
Fast and Guaranteed Tensor Decomposition via Sketching. NIPS 2015: 991-999 - [c43]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Score Function Features for Discriminative Learning. ICLR (Workshop) 2015 - [c42]Hanie Sedghi, Anima Anandkumar:
Provable Methods for Training Neural Networks with Sparse Connectivity. ICLR (Workshop) 2015 - [i44]Anima Anandkumar, Hanie Sedghi:
Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods. CoRR abs/1503.04567 (2015) - [i43]Tejaswi Nimmagadda, Anima Anandkumar:
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models. CoRR abs/1505.00308 (2015) - [i42]Furong Huang, Animashree Anandkumar:
Convolutional Dictionary Learning through Tensor Factorization. CoRR abs/1506.03509 (2015) - [i41]Yining Wang, Hsiao-Yu Fish Tung, Alexander J. Smola, Animashree Anandkumar:
Fast and Guaranteed Tensor Decomposition via Sketching. CoRR abs/1506.04448 (2015) - [i40]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Generalization Bounds for Neural Networks through Tensor Factorization. CoRR abs/1506.08473 (2015) - [i39]Animashree Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan:
Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. CoRR abs/1510.04747 (2015) - 2014
- [j20]Majid Janzamin, Animashree Anandkumar:
High-dimensional covariance decomposition into sparse Markov and independence models. J. Mach. Learn. Res. 15(1): 1549-1591 (2014) - [j19]Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade:
A tensor approach to learning mixed membership community models. J. Mach. Learn. Res. 15(1): 2239-2312 (2014) - [j18]Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15(1): 2773-2832 (2014) - [j17]Pegah Sattari, Maciej Kurant, Animashree Anandkumar, Athina Markopoulou, Michael G. Rabbat:
Active Learning of Multiple Source Multiple Destination Topologies. IEEE Trans. Signal Process. 62(8): 1926-1937 (2014) - [c41]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon:
Learning Sparsely Used Overcomplete Dictionaries. COLT 2014: 123-137 - [c40]Le Song, Animashree Anandkumar, Bo Dai, Bo Xie:
Nonparametric Estimation of Multi-View Latent Variable Models. ICML 2014: 640-648 - [c39]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. NIPS 2014: 1107-1115 - [c38]Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere:
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition. NIPS 2014: 2771-2779 - [i38]Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere:
Guarantees for Multi-Step Stochastic ADMM in High Dimensions. CoRR abs/1402.5131 (2014) - [i37]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates. CoRR abs/1402.5180 (2014) - [i36]Furong Huang, U. N. Niranjan, Animashree Anandkumar:
Integrated Structure and Parameters Learning in Latent Tree Graphical Models. CoRR abs/1406.4566 (2014) - [i35]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings. CoRR abs/1408.0553 (2014) - [i34]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. CoRR abs/1410.7660 (2014) - [i33]Forough Arabshahi, Furong Huang, Animashree Anandkumar, Carter T. Butts:
Modeling Dynamic Social Interactions via Conditional Latent Tree Models. CoRR abs/1411.1132 (2014) - [i32]Anima Anandkumar, Rong Ge, Majid Janzamin:
Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models. CoRR abs/1411.1488 (2014) - [i31]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Score Function Features for Discriminative Learning: Matrix and Tensor Framework. CoRR abs/1412.2863 (2014) - [i30]Hanie Sedghi, Anima Anandkumar:
Provable Tensor Methods for Learning Mixtures of Classifiers. CoRR abs/1412.3046 (2014) - 2013
- [j16]Animashree Anandkumar, Ting He, Chatschik Bisdikian, Dakshi Agrawal:
Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints. Perform. Evaluation 70(12): 1090-1110 (2013) - [j15]Animashree Anandkumar, Avinatan Hassidim, Jonathan A. Kelner:
Topology discovery of sparse random graphs with few participants. Random Struct. Algorithms 43(1): 16-48 (2013) - [c37]Pegah Sattari, Maciej Kurant, Animashree Anandkumar, Athina Markopoulou, Michael G. Rabbat:
Active learning of multiple source multiple destination topologies. CISS 2013: 1-6 - [c36]Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade:
A Tensor Spectral Approach to Learning Mixed Membership Community Models. COLT 2013: 867-881 - [c35]Amod J. G. Anandkumar, Animashree Anandkumar, Sangarapillai Lambotharan, Jonathon A. Chambers:
Robust noncooperative rate-maximization game for MIMO Gaussian interference channels under bounded channel uncertainty. ICASSP 2013: 4819-4823 - [c34]Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade:
Learning Linear Bayesian Networks with Latent Variables. ICML (1) 2013: 249-257 - [c33]Furong Huang, Anima Anandkumar:
FCD: Fast-concurrent-distributed load balancing under switching costs and imperfect observations. INFOCOM 2013: 1896-1904 - [c32]Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade:
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. NIPS 2013: 1986-1994 - [i29]Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade:
A Tensor Spectral Approach to Learning Mixed Membership Community Models. CoRR abs/1302.2684 (2013) - [i28]Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade:
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. CoRR abs/1308.2853 (2013) - [i27]Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Prateek Verma, Animashree Anandkumar:
Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs. CoRR abs/1309.0787 (2013) - [i26]Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli:
Exact Recovery of Sparsely Used Overcomplete Dictionaries. CoRR abs/1309.1952 (2013) - [i25]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon:
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. CoRR abs/1310.7991 (2013) - [i24]Le Song, Animashree Anandkumar, Bo Dai, Bo Xie:
Nonparametric Estimation of Multi-View Latent Variable Models. CoRR abs/1311.3287 (2013) - 2012
- [j14]Animashree Anandkumar, Vincent Y. F. Tan, Furong Huang, Alan S. Willsky:
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion. J. Mach. Learn. Res. 13: 2293-2337 (2012) - [j13]Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, Alan S. Willsky:
Feedback Message Passing for Inference in Gaussian Graphical Models. IEEE Trans. Signal Process. 60(8): 4135-4150 (2012) - [c31]Majid Janzamin, Animashree Anandkumar:
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains. ICML 2012 - [c30]Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu:
A Spectral Algorithm for Latent Dirichlet Allocation. NIPS 2012: 926-934 - [c29]Animashree Anandkumar, Ragupathyraj Valluvan:
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs. NIPS 2012: 1052-1060 - [c28]Animashree Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade:
Learning Mixtures of Tree Graphical Models. NIPS 2012: 1061-1069 - [c27]Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade:
A Method of Moments for Mixture Models and Hidden Markov Models. COLT 2012: 33.1-33.34 - [i23]Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade:
A Method of Moments for Mixture Models and Hidden Markov Models. CoRR abs/1203.0683 (2012) - [i22]Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade:
Learning High-Dimensional Mixtures of Graphical Models. CoRR abs/1203.0697 (2012) - [i21]Animashree Anandkumar, Ragupathyraj Valluvan:
Learning Loopy Graphical Models with Latent Variables: Efficient Methods and Guarantees. CoRR abs/1203.3887 (2012) - [i20]Animashree Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu:
Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation. CoRR abs/1204.6703 (2012) - [i19]Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade:
Learning Linear Bayesian Networks with Latent Variables. CoRR abs/1209.5350 (2012) - [i18]Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor decompositions for learning latent variable models. CoRR abs/1210.7559 (2012) - [i17]Pegah Sattari, Maciej Kurant, Animashree Anandkumar, Athina Markopoulou, Michael G. Rabbat:
Active Learning of Multiple Source Multiple Destination Topologies. CoRR abs/1212.2310 (2012) - 2011
- [j12]Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates. J. Mach. Learn. Res. 12: 1617-1653 (2011) - [j11]Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Learning Latent Tree Graphical Models. J. Mach. Learn. Res. 12: 1771-1812 (2011) - [j10]Animashree Anandkumar, Nithin Michael, Ao Kevin Tang, Ananthram Swami:
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret. IEEE J. Sel. Areas Commun. 29(4): 731-745 (2011) - [j9]Vincent Y. F. Tan, Animashree Anandkumar, Lang Tong, Alan S. Willsky:
A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures. IEEE Trans. Inf. Theory 57(3): 1714-1735 (2011) - [j8]Amod J. G. Anandkumar, Animashree Anandkumar, Sangarapillai Lambotharan, Jonathon A. Chambers:
Robust Rate Maximization Game Under Bounded Channel Uncertainty. IEEE Trans. Veh. Technol. 60(9): 4471-4486 (2011) - [c26]Ting He, Animashree Anandkumar, Dakshi Agrawal:
Index-based sampling policies for tracking dynamic networks under sampling constraints. INFOCOM 2011: 1233-1241 - [c25]Paul Balister, Béla Bollobás, Animashree Anandkumar, Alan S. Willsky:
Energy-latency tradeoff for in-network function computation in random networks. INFOCOM 2011: 1575-1583 - [c24]M. Amin Khajehnejad, Juhwan Yoo, Animashree Anandkumar, Babak Hassibi:
Summary based structures with improved sublinear recovery for compressed sensing. ISIT 2011: 1427-1431 - [c23]Animashree Anandkumar, Vincent Y. F. Tan, Alan S. Willsky:
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions. NIPS 2011: 1863-1871 - [c22]Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang:
Spectral Methods for Learning Multivariate Latent Tree Structure. NIPS 2011: 2025-2033 - [c21]Animashree Anandkumar, Avinatan Hassidim, Jonathan A. Kelner:
Topology discovery of sparse random graphs with few participants. SIGMETRICS 2011: 293-304 - [i16]Paul N. Balister, Béla Bollobás, Animashree Anandkumar, Alan S. Willsky:
Energy-Latency Tradeoff for In-Network Function Computation in Random Networks. CoRR abs/1101.0858 (2011) - [i15]Animashree Anandkumar, Avinatan Hassidim, Jonathan A. Kelner:
Topology Discovery of Sparse Random Graphs With Few Participants. CoRR abs/1102.5063 (2011) - [i14]M. Amin Khajehnejad, Juhwan Yoo, Animashree Anandkumar, Babak Hassibi:
Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing. CoRR abs/1102.5462 (2011) - [i13]Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, Alan S. Willsky:
Feedback Message Passing for Inference in Gaussian Graphical Models. CoRR abs/1105.1853 (2011) - [i12]Animashree Anandkumar, Vincent Y. F. Tan, Alan S. Willsky:
High-Dimensional Gaussian Graphical Model Selection: Tractable Graph Families. CoRR abs/1107.1270 (2011) - [i11]Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang:
Spectral Methods for Learning Multivariate Latent Tree Structure. CoRR abs/1107.1283 (2011) - [i10]Animashree Anandkumar, Vincent Y. F. Tan, Furong Huang, Alan S. Willsky:
High-Dimensional Structure Estimation in Ising Models: Tractable Graph Families. CoRR abs/1107.1736 (2011) - 2010
- [j7]Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Learning Gaussian tree models: analysis of error exponents and extremal structures. IEEE Trans. Signal Process. 58(5): 2701-2714 (2010) - [c20]Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Scaling laws for learning high-dimensional Markov forest distributions. Allerton 2010: 712-718 - [c19]Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Consistent and efficient reconstruction of latent tree models. Allerton 2010: 719-725 - [c18]Amod J. G. Anandkumar, Animashree Anandkumar, Sangarapillai Lambotharan, Jonathon A. Chambers:
Robust rate-maximization game under bounded channel uncertainty. ICASSP 2010: 3158-3161 - [c17]Animashree Anandkumar, Nithin Michael, Ao Tang:
Opportunistic Spectrum Access with Multiple Users: Learning under Competition. INFOCOM 2010: 803-811 - [c16]Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Error exponents for composite hypothesis testing of Markov forest distributions. ISIT 2010: 1613-1617 - [c15]Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, Alan S. Willsky:
Feedback message passing for inference in gaussian graphical models. ISIT 2010: 1683-1687 - [c14]Animashree Anandkumar, Joseph E. Yukich, Alan S. Willsky:
Limit laws for random spatial graphical models. ISIT 2010: 1728-1732 - [i9]Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates. CoRR abs/1005.0766 (2010) - [i8]Animashree Anandkumar, Nithin Michael, Ao Kevin Tang, Ananthram Swami:
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret. CoRR abs/1006.1673 (2010) - [i7]Animashree Anandkumar, Ting He, Chatschik Bisdikian, Dakshi Agrawal:
Seeing Through Black Boxes : Tracking Transactions through Queues under Monitoring Resource Constraints. CoRR abs/1006.1674 (2010) - [i6]Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Learning Latent Tree Graphical Models. CoRR abs/1009.2722 (2010) - [i5]Amod J. G. Anandkumar, Animashree Anandkumar, Sangarapillai Lambotharan, Jonathon A. Chambers:
Robust Rate-Maximization Game Under Bounded Channel Uncertainty. CoRR abs/1011.1566 (2010)
2000 – 2009
- 2009
- [j6]Animashree Anandkumar, Ananthram Swami, Joseph E. Yukich, Lang Tong:
Energy Scaling Laws for Distributed Inference in Random Fusion Networks. IEEE J. Sel. Areas Commun. 27(7): 1203-1217 (2009) - [j5]Animashree Anandkumar, Chatschik Bisdikian, Ting He, Dakshi Agrawal:
Selectively retrofitting monitoring in distributed systems. SIGMETRICS Perform. Evaluation Rev. 37(2): 6-8 (2009) - [j4]Animashree Anandkumar, Lang Tong, Ananthram Swami:
Detection of Gauss-Markov Random Fields With Nearest-Neighbor Dependency. IEEE Trans. Inf. Theory 55(2): 816-827 (2009) - [c13]Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
How do the structure and the parameters of Gaussian tree models affect structure learning? Allerton 2009: 684-691 - [c12]Animashree Anandkumar, Meng Wang, Lang Tong, Ananthram Swami:
Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference. INFOCOM 2009: 2150-2158 - [c11]Vincent Y. F. Tan, Animashree Anandkumar, Lang Tong, Alan S. Willsky:
A large-deviation analysis for the maximum likelihood learning of tree structures. ISIT 2009: 1140-1144 - [c10]Animashree Anandkumar, Alan S. Willsky, Lang Tong:
Detection error exponent for spatially dependent samples in random networks. ISIT 2009: 2882-2886 - [i4]Vincent Y. F. Tan, Animashree Anandkumar, Lang Tong, Alan S. Willsky:
A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures. CoRR abs/0905.0940 (2009) - [i3]Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures. CoRR abs/0909.5216 (2009) - 2008
- [j3]Animashree Anandkumar, Lang Tong, Ananthram Swami:
Distributed Estimation Via Random Access. IEEE Trans. Inf. Theory 54(7): 3175-3181 (2008) - [j2]Animashree Anandkumar, Lang Tong, Ananthram Swami:
Optimal Node Density for Detection in Energy-Constrained Random Networks. IEEE Trans. Signal Process. 56(10-2): 5232-5245 (2008) - [c9]G. Matthew Ezovski, Animashree Anandkumar, Ao Kevin Tang, Lachlan L. H. Andrew:
Min-min times in peer-to-peer file sharing networks. Allerton 2008: 1487-1494 - [c8]Animashree Anandkumar, Lang Tong, Ananthram Swami, Anthony Ephremides:
Minimum Cost Data Aggregation with Localized Processing for Statistical Inference. INFOCOM 2008: 780-788 - [c7]Animashree Anandkumar, Lang Tong, Ananthram Swami, Anthony Ephremides:
Cost-performance tradeoff in multi-hop aggregation for statistical inference. ISIT 2008: 662-666 - [c6]Bikram Sengupta, Nilanjan Banerjee, Animashree Anandkumar, Chatschik Bisdikian:
Non-intrusive transaction monitoring using system logs. NOMS 2008: 879-882 - [c5]Animashree Anandkumar, Chatschik Bisdikian, Dakshi Agrawal:
Tracking in a spaghetti bowl: monitoring transactions using footprints. SIGMETRICS 2008: 133-144 - [i2]Animashree Anandkumar, Joseph E. Yukich, Lang Tong, Ananthram Swami:
Energy Scaling Laws for Distributed Inference in Random Networks. CoRR abs/0809.0686 (2008) - 2007
- [j1]Animashree Anandkumar, Lang Tong:
Type-Based Random Access for Distributed Detection Over Multiaccess Fading Channels. IEEE Trans. Signal Process. 55(10): 5032-5043 (2007) - [c4]Animashree Anandkumar, Lang Tong, Ananthram Swami:
Energy Efficient Routing for Statistical Inference of Markov Random Fields. CISS 2007: 643-648 - [c3]Animashree Anandkumar, Lang Tong, Ananthram Swami:
Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph. ICASSP (3) 2007: 829-832 - [i1]Animashree Anandkumar, Lang Tong, Ananthram Swami:
Detection of Gauss-Markov Random Fields with Nearest-Neighbor Dependency. CoRR abs/0706.1588 (2007) - 2006
- [c2]Animashree Anandkumar, Lang Tong:
Distributed Statistical Inference using Type Based Random Access over Multi-access Fading Channels. CISS 2006: 38-43 - [c1]Animashree Anandkumar, Lang Tong:
A Large Deviation Analysis of Detection Over Multi-Access Channels with Random Number of Sensors. ICASSP (4) 2006: 1097-1100
Coauthor Index
[c201] [c199] [c196] [c193] [i269] [i267] [i264] [i260] [i252] [j47] [c188] [c186] [c167] [c163] [i239] [i232] [i224] [i222] [i219] [i218] [i217] [i216] [j37] [j35] [c158] [c155] [c143] [c135] [d1] [i206] [i199] [i197] [i194] [i191] [i189] [i173] [i170] [c129] [c127] [c126] [c121] [c109] [c99] [i152] [i146] [i145] [i143] [i133] [c86] [c84] [c81] [i126] [i123] [i122] [i121] [i119] [i117] [i115] [i113] [i106] [i99] [c79] [c78] [c74] [i94] [i91] [i88] [i87] [c68] [c67] [c64] [c61] [i76] [i75] [i73] [i69] [i68] [i67] [i64] [i63] [c53] [c52] [i54] [i49] [i46]
Christopher B. Choy
aka: Christopher Bongsoo Choy
aka: Christopher Bongsoo Choy
Daniel Hsu 0001
aka: Daniel J. Hsu
aka: Daniel J. Hsu
Nikola B. Kovachki
aka: Nikola Borislavov Kovachki
aka: Nikola Borislavov Kovachki
Zachary C. Lipton
aka: Zachary Chase Lipton
aka: Zachary Chase Lipton
[j53] [j52] [c202] [c190] [i265] [j48] [j45] [c183] [c180] [c179] [c172] [c171] [i244] [i243] [i241] [i238] [i236] [i235] [i233] [i225] [i215] [c154] [c148] [c145] [c144] [c142] [c133] [c132] [i207] [i201] [i200] [i196] [i186] [i183] [i181] [i179] [i172] [i165] [c107] [c106] [c102] [c101] [e1] [i148] [i141] [i136]
[j53] [c204] [c203] [c191] [c190] [i270] [i265] [i261] [i258] [c183] [c182] [c179] [c177] [c176] [c175] [c173] [c166] [i246] [i243] [i238] [i236] [i225] [i221] [i220] [c153] [c152] [c151] [c150] [c145] [c142] [c138] [c133] [i205] [i202] [i201] [i200] [i195] [i185] [i181] [i175] [c124] [c122] [c120] [c119] [c114] [c106] [c105] [c104] [c102] [i160] [i159] [i157] [i154] [i151] [i150] [i142] [i136] [c93] [c92] [c87] [c83] [i111] [i108] [i107] [i102] [i100] [i80]
[j56] [j52] [c200] [i254] [c185] [c184] [c179] [c169] [i243] [i237] [i234] [i233] [i214] [c150] [c145] [c141] [c139] [c134] [c131] [i208] [i204] [i201] [i195] [i190] [i177] [c125] [c124] [c119] [c118] [c117] [c116] [c115] [i157] [i156] [i153] [i151] [i139] [i135] [i132] [c97] [c83] [c80] [i105] [i103] [i102] [i97] [i95]
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