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2020 – today
- 2024
[c76]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. AISTATS 2024: 2044-2052- [c75]Weida Li, Yaoliang Yu:
Faster Approximation of Probabilistic and Distributional Values via Least Squares. ICLR 2024 - [c74]Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu:
Disguised Copyright Infringement of Latent Diffusion Models. ICML 2024 - [c73]Saber Malekmohammadi, Yaoliang Yu, Yang Cao:
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. ICML 2024 - [c72]Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath
, Yaoliang Yu:
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. SaTML 2024: 327-343 - [i52]Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu:
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. CoRR abs/2402.10150 (2024) - [i51]Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu:
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. CoRR abs/2402.12626 (2024) - [i50]Yiwei Lu, Yaoliang Yu, Xinlin Li, Vahid Partovi Nia:
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers. CoRR abs/2402.17710 (2024) - [i49]Haoye Lu, Spencer Szabados, Yaoliang Yu:
Structure Preserving Diffusion Models. CoRR abs/2402.19369 (2024) - [i48]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. CoRR abs/2404.06516 (2024) - [i47]Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu:
Disguised Copyright Infringement of Latent Diffusion Models. CoRR abs/2404.06737 (2024) - [i46]Saber Malekmohammadi, Yaoliang Yu, Yang Cao:
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. CoRR abs/2406.03519 (2024) - [i45]Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao:
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. CoRR abs/2406.03603 (2024) - [i44]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback. CoRR abs/2408.08395 (2024) - [i43]Ruinan Jin, Xiao Li, Yaoliang Yu, Baoxiang Wang:
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD. CoRR abs/2410.04458 (2024) - [i42]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Last-iterate Convergence in Regularized Graphon Mean Field Game. CoRR abs/2410.08746 (2024) - [i41]Weida Li, Yaoliang Yu:
One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently. CoRR abs/2410.23808 (2024) - [i40]Andre Kassis, Urs Hengartner, Yaoliang Yu:
Unlocking The Potential of Adaptive Attacks on Diffusion-Based Purification. CoRR abs/2411.16598 (2024) - 2023
- [j12]Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu:
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j11]Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu:
Proportional Fairness in Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c71]Ji Xin, Raphael Tang, Zhiying Jiang, Yaoliang Yu, Jimmy Lin:
Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers. ACL (Findings) 2023: 2870-2882 - [c70]Haoye Lu, Daniel Herman, Yaoliang Yu:
Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality. ICLR 2023 - [c69]Yiwei Lu, Gautam Kamath, Yaoliang Yu:
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. ICML 2023: 22856-22879 - [c68]Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart:
Batchnorm Allows Unsupervised Radial Attacks. NeurIPS 2023 - [c67]Dihong Jiang, Sun Sun, Yaoliang Yu:
Functional Renyi Differential Privacy for Generative Modeling. NeurIPS 2023 - [c66]Weida Li, Yaoliang Yu:
Robust Data Valuation with Weighted Banzhaf Values. NeurIPS 2023 - [c65]Yiwei Lu, Yaoliang Yu, Xinlin Li, Vahid Partovi Nia:
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers. NeurIPS 2023 - [i39]Yiwei Lu, Gautam Kamath, Yaoliang Yu:
Exploring the Limits of Indiscriminate Data Poisoning Attacks. CoRR abs/2303.03592 (2023) - 2022
- [j10]Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan-Liu Ma:
Network Comparison with Interpretable Contrastive Network Representation Learning. J. Data Sci. Stat. Vis. 2(5) (2022) - [j9]Guojun Zhang, Pascal Poupart, Yaoliang Yu:
Optimality and Stability in Non-Convex Smooth Games. J. Mach. Learn. Res. 23: 35:1-35:71 (2022) - [j8]Yiwei Lu, Gautam Kamath, Yaoliang Yu:
Indiscriminate Data Poisoning Attacks on Neural Networks. Trans. Mach. Learn. Res. 2022 (2022) - [j7]Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu:
Federated Learning Meets Multi-Objective Optimization. IEEE Trans. Netw. Sci. Eng. 9(4): 2039-2051 (2022) - [c64]Dihong Jiang, Sun Sun, Yaoliang Yu:
Revisiting flow generative models for Out-of-distribution detection. ICLR 2022 - [i38]Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu:
Equality Is Not Equity: Proportional Fairness in Federated Learning. CoRR abs/2202.01666 (2022) - [i37]Yiwei Lu, Gautam Kamath, Yaoliang Yu:
Indiscriminate Data Poisoning Attacks on Neural Networks. CoRR abs/2204.09092 (2022) - [i36]Qinghua Zheng, Jihong Wang, Minnan Luo, Yaoliang Yu, Jundong Li, Lina Yao, Xiaojun Chang:
Towards Explanation for Unsupervised Graph-Level Representation Learning. CoRR abs/2205.09934 (2022) - [i35]Artur Back de Luca, Guojun Zhang, Xi Chen, Yaoliang Yu:
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation. CoRR abs/2206.09979 (2022) - [i34]Ji Xin, Raphael Tang, Zhiying Jiang, Yaoliang Yu, Jimmy Lin:
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers. CoRR abs/2208.00483 (2022) - [i33]Dihong Jiang, Guojun Zhang, Mahdi Karami, Xi Chen, Yunfeng Shao, Yaoliang Yu:
DP2-VAE: Differentially Private Pre-trained Variational Autoencoders. CoRR abs/2208.03409 (2022) - 2021
- [c63]Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin:
The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing. ACL/IJCNLP (1) 2021: 1040-1051 - [c62]Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin:
BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression. EACL 2021: 91-104 - [c61]Hung Viet Pham, Mijung Kim, Lin Tan, Yaoliang Yu, Nachiappan Nagappan:
DEVIATE: A Deep Learning Variance Testing Framework. ASE 2021: 1286-1290 - [c60]Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao:
Posterior Differential Regularization with f-divergence for Improving Model Robustness. NAACL-HLT 2021: 1078-1089 - [c59]Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart:
Quantifying and Improving Transferability in Domain Generalization. NeurIPS 2021: 10957-10970 - [c58]Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia:
Demystifying and Generalizing BinaryConnect. NeurIPS 2021: 13202-13216 - [c57]Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing Xu, Vahid Partovi Nia:
S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks. NeurIPS 2021: 14555-14566 - [c56]Shangshu Qian, Hung Viet Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, Sameena Shah:
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. NeurIPS 2021: 30211-30227 - [c55]Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu:
Splitting Algorithms for Federated Learning. PKDD/ECML Workshops (1) 2021: 159-176 - [i32]Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart:
Quantifying and Improving Transferability in Domain Generalization. CoRR abs/2106.03632 (2021) - [i31]Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing Xu, Vahid Partovi Nia:
S3: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks. CoRR abs/2107.03453 (2021) - [i30]Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu:
An Operator Splitting View of Federated Learning. CoRR abs/2108.05974 (2021) - [i29]Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia:
Demystifying and Generalizing BinaryConnect. CoRR abs/2110.13220 (2021) - 2020
- [j6]Yi Shi, Zehua Guo, Xianbin Su, Luming Meng, Mingxuan Zhang, Jing Sun, Chao Wu, Minhua Zheng, Xueyin Shang, Xin Zou, Wangqiu Cheng, Yaoliang Yu, Yujia Cai, Chaoyi Zhang, Weidong Cai, Lin-Tai Da, Guang He, Zeguang Han:
DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning. Bioinform. 36(19): 4894-4901 (2020) - [c54]Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, Jimmy Lin:
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. ACL 2020: 2246-2251 - [c53]Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yaoliang Yu, Jimmy Lin:
Showing Your Work Doesn't Always Work. ACL 2020: 2766-2772 - [c52]Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu:
On Minimax Optimality of GANs for Robust Mean Estimation. AISTATS 2020: 4541-4551 - [c51]Ji Xin, Rodrigo Frassetto Nogueira, Yaoliang Yu, Jimmy Lin:
Early Exiting BERT for Efficient Document Ranking. SustaiNLP@EMNLP 2020: 83-88 - [c50]Guojun Zhang, Yaoliang Yu:
Convergence of Gradient Methods on Bilinear Zero-Sum Games. ICLR 2020 - [c49]Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus A. Brubaker:
Tails of Lipschitz Triangular Flows. ICML 2020: 4673-4681 - [c48]Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang:
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. ICML 2020: 6532-6542 - [c47]Kaiwen Wu, Allen Houze Wang, Yaoliang Yu:
Stronger and Faster Wasserstein Adversarial Attacks. ICML 2020: 10377-10387 - [c46]Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu:
Unsupervised Multilingual Alignment using Wasserstein Barycenter. IJCAI 2020: 3702-3708 - [c45]Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, Nachiappan Nagappan:
Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance. ASE 2020: 771-783 - [i28]Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu:
Unsupervised Multilingual Alignment using Wasserstein Barycenter. CoRR abs/2002.00743 (2020) - [i27]Guojun Zhang, Pascal Poupart, Yaoliang Yu:
Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization. CoRR abs/2002.11875 (2020) - [i26]Allen Houze Wang, Priyank Jaini, Yaoliang Yu, Pascal Poupart:
Complete Hierarchy of Relaxation for Constrained Signomial Positivity. CoRR abs/2003.03731 (2020) - [i25]Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang:
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. CoRR abs/2004.12209 (2020) - [i24]Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, Jimmy Lin:
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. CoRR abs/2004.12993 (2020) - [i23]Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yaoliang Yu, Jimmy Lin:
Showing Your Work Doesn't Always Work. CoRR abs/2004.13705 (2020) - [i22]Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan-Liu Ma:
Interpretable Contrastive Learning for Networks. CoRR abs/2005.12419 (2020) - [i21]Tim Dockhorn, James A. Ritchie, Yaoliang Yu, Iain Murray:
Density Deconvolution with Normalizing Flows. CoRR abs/2006.09396 (2020) - [i20]Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu:
FedMGDA+: Federated Learning meets Multi-objective Optimization. CoRR abs/2006.11489 (2020) - [i19]Guojun Zhang, Kaiwen Wu, Pascal Poupart, Yaoliang Yu:
Newton-type Methods for Minimax Optimization. CoRR abs/2006.14592 (2020) - [i18]Kaiwen Wu, Allen Houze Wang, Yaoliang Yu:
Stronger and Faster Wasserstein Adversarial Attacks. CoRR abs/2008.02883 (2020) - [i17]Zejiang Shen, Jian Zhao, Melissa Dell, Yaoliang Yu, Weining Li:
OLALA: Object-Level Active Learning Based Layout Annotation. CoRR abs/2010.01762 (2020) - [i16]Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao:
Posterior Differential Regularization with f-divergence for Improving Model Robustness. CoRR abs/2010.12638 (2020)
2010 – 2019
- 2019
- [c44]Sun Sun, Yaoliang Yu:
Least Squares Estimation of Weakly Convex Functions. AISTATS 2019: 2271-2280 - [c43]Ji Xin, Jimmy Lin, Yaoliang Yu:
What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons. EMNLP/IJCNLP (1) 2019: 5822-5829 - [c42]Priyank Jaini, Kira A. Selby, Yaoliang Yu:
Sum-of-Squares Polynomial Flow. ICML 2019: 3009-3018 - [c41]Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. ICML 2019: 4424-4434 - [c40]Jingjing Wang, Sun Sun, Yaoliang Yu:
Multivariate Triangular Quantile Maps for Novelty Detection. NeurIPS 2019: 5061-5072 - [i15]Priyank Jaini, Kira A. Selby, Yaoliang Yu:
Sum-of-Squares Polynomial Flow. CoRR abs/1905.02325 (2019) - [i14]Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. CoRR abs/1905.06125 (2019) - [i13]Priyank Jaini, Ivan Kobyzev, Marcus A. Brubaker, Yaoliang Yu:
Tails of Triangular Flows. CoRR abs/1907.04481 (2019) - [i12]Kaiwen Wu, Yaoliang Yu:
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. CoRR abs/1907.11780 (2019) - [i11]Guojun Zhang, Yaoliang Yu:
Convergence Behaviour of Some Gradient-Based Methods on Bilinear Games. CoRR abs/1908.05699 (2019) - [i10]Achyudh Ram, Ji Xin, Meiyappan Nagappan, Yaoliang Yu, Rocío Cabrera Lozoya, Antonino Sabetta, Jimmy Lin:
Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits. CoRR abs/1911.07620 (2019) - 2018
- [j5]Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing:
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. J. Mach. Learn. Res. 19: 19:1-19:32 (2018) - [c39]Pengtao Xie, Jin Kyu Kim, Qirong Ho, Yaoliang Yu, Eric P. Xing:
Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. SoCC 2018: 1-13 - [c38]Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu:
Inductive Two-layer Modeling with Parametric Bregman Transfer. ICML 2018: 1622-1631 - [c37]Priyank Jaini, Pascal Poupart, Yaoliang Yu:
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. NeurIPS 2018: 7136-7145 - 2017
- [j4]Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Generalized Conditional Gradient for Sparse Estimation. J. Mach. Learn. Res. 18: 144:1-144:46 (2017) - [j3]Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing:
Semantic Pooling for Complex Event Analysis in Untrimmed Videos. IEEE Trans. Pattern Anal. Mach. Intell. 39(8): 1617-1632 (2017) - [c36]Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing:
Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. CVPR 2017: 5948-5956 - [c35]Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy:
Dropout with Expectation-linear Regularization. ICLR (Poster) 2017 - [c34]Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing:
Learning Latent Space Models with Angular Constraints. ICML 2017: 3799-3810 - [c33]Xiaojun Chang, Yaoliang Yu, Yi Yang:
Robust Top-k Multiclass SVM for Visual Category Recognition. KDD 2017: 75-83 - [c32]Zhan Shi, Xinhua Zhang, Yaoliang Yu:
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction. NIPS 2017: 6031-6041 - [c31]Junming Yin, Yaoliang Yu:
Convex-constrained Sparse Additive Modeling and Its Extensions. UAI 2017 - [i9]Junming Yin, Yaoliang Yu:
Convex-constrained Sparse Additive Modeling and Its Extensions. CoRR abs/1705.00687 (2017) - [i8]Shrinu Kushagra, Nicole McNabb, Yaoliang Yu, Shai Ben-David:
Provably noise-robust, regularised k-means clustering. CoRR abs/1711.11247 (2017) - 2016
- [c30]Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing:
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. AISTATS 2016: 713-722 - [c29]Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans:
Scalable and Sound Low-Rank Tensor Learning. AISTATS 2016: 1114-1123 - [c28]Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing:
They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers. CVPR 2016: 1884-1893 - [c27]Marc T. Law, Yaoliang Yu, Matthieu Cord, Eric P. Xing:
Closed-Form Training of Mahalanobis Distance for Supervised Clustering. CVPR 2016: 3909-3917 - [c26]Kirthevasan Kandasamy, Yaoliang Yu:
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. ICML 2016: 69-78 - [c25]Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen:
Convex Two-Layer Modeling with Latent Structure. NIPS 2016: 1280-1288 - [c24]Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. UAI 2016 - [r1]Yaoliang Yu:
Online Learning and Optimization. Encyclopedia of Algorithms 2016: 1443-1448 - [i7]Kirthevasan Kandasamy, Yaoliang Yu:
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. CoRR abs/1602.00287 (2016) - [i6]Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy:
Dropout with Expectation-linear Regularization. CoRR abs/1609.08017 (2016) - 2015
- [j2]Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu:
Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Trans. Big Data 1(2): 49-67 (2015) - [c23]Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric P. Xing:
Minimizing Nonconvex Non-Separable Functions. AISTATS 2015 - [c22]Xiaojun Chang, Yi Yang, Eric P. Xing, Yaoliang Yu:
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. ICML 2015: 1348-1357 - [c21]Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Eric P. Xing, Yaoliang Yu:
Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. IJCAI 2015: 2234-2240 - [c20]Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu:
Petuum: A New Platform for Distributed Machine Learning on Big Data. KDD 2015: 1335-1344 - [c19]Xun Zheng, Yaoliang Yu, Eric P. Xing:
Linear Time Samplers for Supervised Topic Models using Compositional Proposals. KDD 2015: 1523-1532 - [c18]Xiaojun Chang, Yaoliang Yu, Yi Yang, Alexander G. Hauptmann:
Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision. ACM Multimedia 2015: 581-590 - [i5]Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra:
Efficient Structured Matrix Rank Minimization. CoRR abs/1509.02447 (2015) - [i4]Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Distributed Machine Learning via Sufficient Factor Broadcasting. CoRR abs/1511.08486 (2015) - 2014
- [c17]Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra:
Efficient Structured Matrix Rank Minimization. NIPS 2014: 1350-1358 - [i3]Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Generalized Conditional Gradient for Sparse Estimation. CoRR abs/1410.4828 (2014) - 2013
- [c16]Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvári:
Characterizing the Representer Theorem. ICML (1) 2013: 570-578 - [c15]Xinhua Zhang, Yaoliang Yu, Dale Schuurmans:
Polar Operators for Structured Sparse Estimation. NIPS 2013: 82-90 - [c14]Yaoliang Yu:
On Decomposing the Proximal Map. NIPS 2013: 91-99 - [c13]Yaoliang Yu:
Better Approximation and Faster Algorithm Using the Proximal Average. NIPS 2013: 458-466 - 2012
- [c12]James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans:
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. ICML 2012 - [c11]Yaoliang Yu, Csaba Szepesvári:
Analysis of Kernel Mean Matching under Covariate Shift. ICML 2012 - [c10]Martha White, Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Convex Multi-view Subspace Learning. NIPS 2012: 1682-1690 - [c9]Yaoliang Yu, Özlem Aslan, Dale Schuurmans:
A Polynomial-time Form of Robust Regression. NIPS 2012: 2492-2500 - [c8]Xinhua Zhang, Yaoliang Yu, Dale Schuurmans:
Accelerated Training for Matrix-norm Regularization: A Boosting Approach. NIPS 2012: 2915-2923 - [i2]Yaoliang Yu, Dale Schuurmans:
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. CoRR abs/1202.3772 (2012) - [i1]Yaoliang Yu, Csaba Szepesvári:
Analysis of Kernel Mean Matching under Covariate Shift. CoRR abs/1206.4650 (2012) - 2011
- [j1]Yaoliang Yu, Jiayan Jiang, Liming Zhang:
Distance metric learning by minimal distance maximization. Pattern Recognit. 44(3): 639-649 (2011) - [c7]Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans:
Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. AAAI 2011: 567-573 - [c6]Yaoliang Yu, Dale Schuurmans:
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. UAI 2011: 778-785 - 2010
- [c5]Yaoliang Yu, Min Yang, Linli Xu, Martha White, Dale Schuurmans:
Relaxed Clipping: A Global Training Method for Robust Regression and Classification. NIPS 2010: 2532-2540
2000 – 2009
- 2009
- [c4]Yaoliang Yu, Yuxi Li, Dale Schuurmans, Csaba Szepesvári:
A General Projection Property for Distribution Families. NIPS 2009: 2232-2240 - 2007
- [c3]Peng Guan, Yaoliang Yu, Liming Zhang:
A Novel Facial Feature Point Localization Method on 3D Faces. ICIP (3) 2007: 69-72 - [c2]Yaoliang Yu, Peng Guan, Liming Zhang:
Extensions of Manifold Learning Algorithms in Kernel Feature Space. ISNN (1) 2007: 449-454 - [c1]Peng Guan, Yaoliang Yu, Liming Zhang:
Discriminant Analysis with Label Constrained Graph Partition. ISNN (2) 2007: 671-679
Coauthor Index
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, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
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Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-09 13:02 CET by the dblp team
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