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ORCIDPrateek Jain 0002
Person information
- affiliation: Google
- affiliation (former): Microsoft Research India
- affiliation (former): UT Austin, USA
- affiliation (former): IIT Kanpur, India
Other persons with the same name
- Prateek Jain — disambiguation page
- Prateek Jain 0001 — Nuance Research, Sunnyvale, CA, USA (and 2 more)
- Prateek Jain 0003 — Bell Labs, USA (and 1 more)
- Prateek Jain 0004 — Dhirubhai Ambani Institute of Information and Communication Technology
- Prateek Jain 0005 — University of California, Irvine, USA
- Prateek Jain 0006
![[0000-0002-8191-9785]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-8191-9785")
— Malaviya National Institute of Technology, Jaipur, India (and 1 more) - Prateek Jain 0007 — Jaypee Institute of Information Technology University, Noida, India
- Prateek Jain 0008 — Indian Institute of Technology Indore, Madhya Pradesh, India
- Prateek Jain 0009 — Worcester Polytechnic Institute, USA
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2020 – today
- 2025
[i107]Sahil Goyal, Debapriya Tula, Gagan Jain, Pradeep Shenoy, Prateek Jain, Sujoy Paul:
Masked Generative Nested Transformers with Decode Time Scaling. CoRR abs/2502.00382 (2025)- [i106]Pranav Ajit Nair, Puranjay Datta, Jeff Dean, Prateek Jain, Aditya Kusupati:
Matryoshka Quantization. CoRR abs/2502.06786 (2025) - 2024
- [c127]Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava:
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components. AISTATS 2024: 1702-1710 - [c126]Anshul Nasery, Hardik Shah, Arun Sai Suggala, Prateek Jain:
End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates. CVPR Workshops 2024: 5866-5877 - [c125]Rajat Koner, Gagan Jain, Prateek Jain, Volker Tresp, Sujoy Paul:
LookupViT: Compressing Visual Information to a Limited Number of Tokens. ECCV (86) 2024: 322-337 - [c124]Nilesh Gupta, Devvrit, Ankit Singh Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S. Dhillon:
Dual-Encoders for Extreme Multi-label Classification. ICLR 2024 - [c123]Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
Tandem Transformers for Inference Efficient LLMs. ICML 2024 - [c122]Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hanna Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain:
MatFormer: Nested Transformer for Elastic Inference. NeurIPS 2024 - [c121]Gagan Jain, Nidhi Hegde, Aditya Kusupati, Arsha Nagrani, Shyamal Buch, Prateek Jain, Anurag Arnab, Sujoy Paul:
Mixture of Nested Experts: Adaptive Processing of Visual Tokens. NeurIPS 2024 - [c120]Yerram Varun, Rahul Madhavan, Sravanti Addepalli, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Time-Reversal Provides Unsupervised Feedback to LLMs. NeurIPS 2024 - [i105]Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
Tandem Transformers for Inference Efficient LLMs. CoRR abs/2402.08644 (2024) - [i104]Yashas Samaga, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference. CoRR abs/2402.09360 (2024) - [i103]Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer, Jeremy R. Cole, Kai Hui, Michael Boratko, Rajvi Kapadia, Wen Ding, Yi Luan, Sai Meher Karthik Duddu, Gustavo Hernández Ábrego, Weiqiang Shi, Nithi Gupta, Aditya Kusupati, Prateek Jain, Siddhartha Reddy Jonnalagadda, Ming-Wei Chang, Iftekhar Naim:
Gecko: Versatile Text Embeddings Distilled from Large Language Models. CoRR abs/2403.20327 (2024) - [i102]Rajat Koner, Gagan Jain, Prateek Jain, Volker Tresp, Sujoy Paul:
LookupViT: Compressing visual information to a limited number of tokens. CoRR abs/2407.12753 (2024) - [i101]Gagan Jain, Nidhi Hegde, Aditya Kusupati, Arsha Nagrani, Shyamal Buch, Prateek Jain, Anurag Arnab, Sujoy Paul:
Mixture of Nested Experts: Adaptive Processing of Visual Tokens. CoRR abs/2407.19985 (2024) - [i100]Yerram Varun, Rahul Madhavan, Sravanti Addepalli, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Time-Reversal Provides Unsupervised Feedback to LLMs. CoRR abs/2412.02626 (2024) - [i99]Sravanti Addepalli, Yerram Varun, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts? CoRR abs/2412.03235 (2024) - 2023
- [c119]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Optimal Algorithms for Latent Bandits with Cluster Structure. AISTATS 2023: 7540-7577 - [c118]Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain:
Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks. ICLR 2023 - [c117]Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain:
Treeformer: Dense Gradient Trees for Efficient Attention Computation. ICLR 2023 - [c116]Soumyabrata Pal, Prateek Jain:
Online Low Rank Matrix Completion. ICLR 2023 - [c115]Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang:
Multi-Task Differential Privacy Under Distribution Skew. ICML 2023: 17784-17807 - [c114]Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain:
Multi-User Reinforcement Learning with Low Rank Rewards. ICML 2023: 25627-25659 - [c113]Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala:
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency. NeurIPS 2023 - [c112]Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli:
Simplicity Bias in 1-Hidden Layer Neural Networks. NeurIPS 2023 - [c111]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. NeurIPS 2023 - [c110]Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham M. Kakade, Prateek Jain, Ali Farhadi:
AdANNS: A Framework for Adaptive Semantic Search. NeurIPS 2023 - [i98]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Optimal Algorithms for Latent Bandits with Cluster Structure. CoRR abs/2301.07040 (2023) - [i97]Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala:
Near Optimal Private and Robust Linear Regression. CoRR abs/2301.13273 (2023) - [i96]Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli:
Simplicity Bias in 1-Hidden Layer Neural Networks. CoRR abs/2302.00457 (2023) - [i95]Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, Li Zhang:
Multi-Task Differential Privacy Under Distribution Skew. CoRR abs/2302.07975 (2023) - [i94]Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham M. Kakade, Prateek Jain, Ali Farhadi:
AdANNS: A Framework for Adaptive Semantic Search. CoRR abs/2305.19435 (2023) - [i93]Anshul Nasery, Hardik Shah, Arun Sai Suggala, Prateek Jain:
End-to-End Neural Network Compression via 𝓁1/𝓁2 Regularized Latency Surrogates. CoRR abs/2306.05785 (2023) - [i92]Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain:
MatFormer: Nested Transformer for Elastic Inference. CoRR abs/2310.07707 (2023) - [i91]Ramnath Kumar, Anshul Mittal, Nilesh Gupta, Aditya Kusupati, Inderjit S. Dhillon, Prateek Jain:
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval. CoRR abs/2310.08891 (2023) - [i90]Nilesh Gupta, Devvrit Khatri, Ankit Singh Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S. Dhillon:
Efficacy of Dual-Encoders for Extreme Multi-Label Classification. CoRR abs/2310.10636 (2023) - [i89]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. CoRR abs/2311.03376 (2023) - 2022
- [j14]Ajaykrishna Karthikeyan, Naman Jain, Nagarajan Natarajan, Prateek Jain:
Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent. Trans. Mach. Learn. Res. 2022 (2022) - [c109]Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. AISTATS 2022: 11145-11168 - [c108]Prateek Varshney, Abhradeep Thakurta, Prateek Jain:
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. COLT 2022: 1126-1166 - [c107]Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Mysore Nagaraj, Praneeth Netrapalli:
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. ICLR 2022 - [c106]S. Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam:
IGLU: Efficient GCN Training via Lazy Updates. ICLR 2022 - [c105]Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS 2022 - [c104]Devvrit, Aditya Sinha, Inderjit S. Dhillon, Prateek Jain:
S3GC: Scalable Self-Supervised Graph Clustering. NeurIPS 2022 - [c103]Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, Ali Farhadi:
Matryoshka Representation Learning. NeurIPS 2022 - [c102]Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh:
DP-PCA: Statistically Optimal and Differentially Private PCA. NeurIPS 2022 - [i88]Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. CoRR abs/2202.04598 (2022) - [i87]Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, Ali Farhadi:
Matryoshka Representations for Adaptive Deployment. CoRR abs/2205.13147 (2022) - [i86]Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh:
DP-PCA: Statistically Optimal and Differentially Private PCA. CoRR abs/2205.13709 (2022) - [i85]Kushal Majmundar, Sachin Goyal, Praneeth Netrapalli, Prateek Jain:
MET: Masked Encoding for Tabular Data. CoRR abs/2206.08564 (2022) - [i84]Prateek Varshney, Abhradeep Thakurta, Prateek Jain:
(Nearly) Optimal Private Linear Regression via Adaptive Clipping. CoRR abs/2207.04686 (2022) - [i83]Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain:
Treeformer: Dense Gradient Trees for Efficient Attention Computation. CoRR abs/2208.09015 (2022) - [i82]Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain:
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization. CoRR abs/2208.09139 (2022) - [i81]Prateek Jain, Soumyabrata Pal:
Online Low Rank Matrix Completion. CoRR abs/2209.03997 (2022) - [i80]Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu, Praneeth Netrapalli, Prateek Jain:
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks. CoRR abs/2210.01360 (2022) - [i79]Soumyabrata Pal, Prateek Varshney, Prateek Jain, Abhradeep Guha Thakurta, Gagan Madan, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava:
Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition. CoRR abs/2210.03505 (2022) - [i78]Naman Agarwal, Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Multi-User Reinforcement Learning with Low Rank Rewards. CoRR abs/2210.05355 (2022) - 2021
- [j13]Prateek Jain, Dheeraj M. Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. SIAM J. Optim. 31(2): 1108-1130 (2021) - [c101]Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. ICML 2021: 1877-1887 - [c100]Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain:
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. ICML 2021: 9255-9264 - [c99]Harshay Shah, Prateek Jain, Praneeth Netrapalli:
Do Input Gradients Highlight Discriminative Features? NeurIPS 2021: 2046-2059 - [c98]Suhas S. Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli:
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. NeurIPS 2021: 8518-8531 - [c97]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Statistically and Computationally Efficient Linear Meta-representation Learning. NeurIPS 2021: 18487-18500 - [c96]Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi:
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. NeurIPS 2021: 23900-23913 - [c95]Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta:
Differentially Private Model Personalization. NeurIPS 2021: 29723-29735 - [c94]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Streaming Linear System Identification with Reverse Experience Replay. NeurIPS 2021: 30140-30152 - [i77]Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain:
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization. CoRR abs/2102.07387 (2021) - [i76]Ajaykrishna Karthikeyan, Naman Jain, Nagarajan Natarajan, Prateek Jain:
Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent. CoRR abs/2102.07567 (2021) - [i75]Harshay Shah, Prateek Jain, Praneeth Netrapalli:
Do Input Gradients Highlight Discriminative Features? CoRR abs/2102.12781 (2021) - [i74]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Streaming Linear System Identification with Reverse Experience Replay. CoRR abs/2103.05896 (2021) - [i73]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Sample Efficient Linear Meta-Learning by Alternating Minimization. CoRR abs/2105.08306 (2021) - [i72]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. CoRR abs/2105.11558 (2021) - [i71]Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi:
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. CoRR abs/2106.01487 (2021) - [i70]Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. CoRR abs/2106.08882 (2021) - [i69]Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. CoRR abs/2107.09802 (2021) - [i68]S. Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam:
IGLU: Efficient GCN Training via Lazy Updates. CoRR abs/2109.13995 (2021) - [i67]Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. CoRR abs/2110.08440 (2021) - [i66]Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain:
Node-Level Differentially Private Graph Neural Networks. CoRR abs/2111.15521 (2021) - 2020
- [j12]Anthony Man-Cho So, Prateek Jain, Wing-Kin Ma, Gesualdo Scutari:
Nonconvex Optimization for Signal Processing and Machine Learning [From the Guest Editors]. IEEE Signal Process. Mag. 37(5): 15-17 (2020) - [c93]Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain:
DROCC: Deep Robust One-Class Classification. ICML 2020: 3711-3721 - [c92]Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham M. Kakade, Ali Farhadi:
Soft Threshold Weight Reparameterization for Learnable Sparsity. ICML 2020: 5544-5555 - [c91]Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli:
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. NeurIPS 2020 - [c90]Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain:
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. NeurIPS 2020 - [c89]Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS 2020 - [c88]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. NeurIPS 2020 - [i65]Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham M. Kakade, Ali Farhadi:
Soft Threshold Weight Reparameterization for Learnable Sparsity. CoRR abs/2002.03231 (2020) - [i64]Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain:
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. CoRR abs/2002.11921 (2020) - [i63]Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain:
DROCC: Deep Robust One-Class Classification. CoRR abs/2002.12718 (2020) - [i62]Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. CoRR abs/2006.07710 (2020) - [i61]Guy Bresler, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Xian Wu:
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. CoRR abs/2006.08916 (2020) - [i60]Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar:
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. CoRR abs/2006.14211 (2020) - [i59]Nagarajan Natarajan, Ajaykrishna Karthikeyan, Prateek Jain, Ivan Radicek, Sriram K. Rajamani, Sumit Gulwani, Johannes Gehrke:
Programming by Rewards. CoRR abs/2007.06835 (2020) - [i58]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. CoRR abs/2010.01848 (2020)
2010 – 2019
- 2019
- [c87]Vivek Gupta, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain, Piyush Rai:
Distributional Semantics Meets Multi-Label Learning. AAAI 2019: 3747-3754 - [c86]Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar:
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. AISTATS 2019: 313-322 - [c85]Nagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain, Sumit Gulwani:
Learning Natural Programs from a Few Examples in Real-Time. AISTATS 2019: 1714-1722 - [c84]Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama:
Cost aware Inference for IoT Devices. AISTATS 2019: 2770-2779 - [c83]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. COLT 2019: 1752-1755 - [c82]Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain:
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. COLT 2019: 2892-2897 - [c81]Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli:
Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT 2019: 3190-3193 - [c80]Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli:
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. ICML 2019: 4703-4711 - [c79]Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon:
Provable Non-linear Inductive Matrix Completion. NeurIPS 2019: 11435-11445 - [c78]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. NeurIPS 2019: 12659-12670 - [c77]Don Kurian Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama, Harsha Vardhan Simhadri, Prateek Jain:
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices. NeurIPS 2019: 12896-12906 - [c76]Shishir G. Patil, Don Kurian Dennis, Chirag Pabbaraju, Nadeem Shaheer, Harsha Vardhan Simhadri, Vivek Seshadri, Manik Varma, Prateek Jain:
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users. UIST 2019: 403-415 - [i57]Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma:
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. CoRR abs/1901.02358 (2019) - [i56]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. CoRR abs/1903.01463 (2019) - [i55]Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain:
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. CoRR abs/1903.08192 (2019) - [i54]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. CoRR abs/1904.12443 (2019) - [i53]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. CoRR abs/1907.01543 (2019) - [i52]Chirag Pabbaraju, Prateek Jain:
Learning Functions over Sets via Permutation Adversarial Networks. CoRR abs/1907.05638 (2019) - [i51]Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain:
OASIS: ILP-Guided Synthesis of Loop Invariants. CoRR abs/1911.11728 (2019) - 2018
- [j11]Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit Gulwani, Todd D. Millstein:
FlashProfile: a framework for synthesizing data profiles. Proc. ACM Program. Lang. 2(OOPSLA): 150:1-150:28 (2018) - [c75]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT 2018: 545-604 - [c74]Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli:
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. COLT 2018: 3243-3270 - [c73]Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani:
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. ICLR (Poster) 2018 - [c72]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR 2018 - [c71]Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Differentially Private Matrix Completion Revisited. ICML 2018: 2220-2229 - [c70]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. ITA 2018: 1-9 - [c69]Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma:
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. NeurIPS 2018: 9031-9042 - [c68]Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli:
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. NeurIPS 2018: 10837-10847 - [c67]Don Kurian Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, Prateek Jain:
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices. NeurIPS 2018: 10976-10987 - [i50]Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli:
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. CoRR abs/1803.00186 (2018) - [i49]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. CoRR abs/1803.05591 (2018) - [i48]Ashwin J. Vijayakumar, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani:
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. CoRR abs/1804.01186 (2018) - [i47]Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon:
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks. CoRR abs/1805.10477 (2018) - 2017
- [j10]Prateek Jain, Purushottam Kar:
Non-convex Optimization for Machine Learning. Found. Trends Mach. Learn. 10(3-4): 142-336 (2017) - [j9]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. J. Mach. Learn. Res. 18: 223:1-223:42 (2017) - [j8]Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Partial Hard Thresholding. IEEE Trans. Inf. Theory 63(5): 3029-3038 (2017) - [c66]Apoorv Aggarwal, Sandip Ghoshal, Ankith M. S. Shetty, Suhit Sinha, Ganesh Ramakrishnan, Purushottam Kar, Prateek Jain:
Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning. AAAI 2017: 1698-1704 - [c65]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. AISTATS 2017: 479-488 - [c64]Sumit Gulwani, Prateek Jain:
Programming by Examples: PL Meets ML. APLAS 2017: 3-20 - [c63]Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli:
Thresholding Based Outlier Robust PCA. COLT 2017: 593-628 - [c62]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). FSTTCS 2017: 2:1-2:10 - [c61]Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan:
Active Heteroscedastic Regression. ICML 2017: 694-702 - [c60]Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain:
Nearly Optimal Robust Matrix Completion. ICML 2017: 797-805 - [c59]Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain:
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices. ICML 2017: 1331-1340 - [c58]Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon:
Recovery Guarantees for One-hidden-layer Neural Networks. ICML 2017: 4140-4149 - [c57]Kai Zhong, Prateek Jain, Ashish Kapoor:
Fast second-order cone programming for safe mission planning. ICRA 2017: 79-86 - [c56]Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar:
Consistent Robust Regression. NIPS 2017: 2110-2119 - [c55]Aditi Raghunathan, Prateek Jain, Ravishankar Krishnaswamy:
Learning Mixture of Gaussians with Streaming Data. NIPS 2017: 6605-6614 - [i46]Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli:
Thresholding based Efficient Outlier Robust PCA. CoRR abs/1702.05571 (2017) - [i45]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent. CoRR abs/1704.08227 (2017) - [i44]Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon:
Recovery Guarantees for One-hidden-layer Neural Networks. CoRR abs/1706.03175 (2017) - [i43]Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain:
Learning Mixture of Gaussians with Streaming Data. CoRR abs/1707.02391 (2017) - [i42]Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit Gulwani, Todd D. Millstein:
FlashProfile: Interactive Synthesis of Syntactic Profiles. CoRR abs/1709.05725 (2017) - [i41]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). CoRR abs/1710.09430 (2017) - [i40]Prateek Jain, Purushottam Kar:
Non-convex Optimization for Machine Learning. CoRR abs/1712.07897 (2017) - [i39]Prateek Jain, Om Thakkar, Abhradeep Thakurta:
Differentially Private Matrix Completion, Revisited. CoRR abs/1712.09765 (2017) - 2016
- [j7]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli:
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. 26(4): 2775-2799 (2016) - [c54]Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan:
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. AISTATS 2016: 268-276 - [c53]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. COLT 2016: 1147-1164 - [c52]Vidyadhar Rao, Prateek Jain, C. V. Jawahar:
Diverse Yet Efficient Retrieval using Locality Sensitive Hashing. ICMR 2016: 189-196 - [c51]Prateek Jain, Nikhil Rao, Inderjit S. Dhillon:
Structured Sparse Regression via Greedy Hard Thresholding. NIPS 2016: 1516-1524 - [c50]Kai Zhong, Prateek Jain, Inderjit S. Dhillon:
Mixed Linear Regression with Multiple Components. NIPS 2016: 2190-2198 - [c49]Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty:
Selective inference for group-sparse linear models. NIPS 2016: 2469-2477 - [c48]Nagarajan Natarajan, Prateek Jain:
Regret Bounds for Non-decomposable Metrics with Missing Labels. NIPS 2016: 2874-2882 - [e1]Madhav V. Marathe, Mukesh K. Mohania, Mausam, Prateek Jain:
Proceedings of the 3rd IKDD Conference on Data Science, CODS 2016, Pune, India, March 13-16, 2016. ACM 2016, ISBN 978-1-4503-4217-9 [contents] - [i38]Prateek Jain, Nikhil Rao, Inderjit S. Dhillon:
Structured Sparse Regression via Greedy Hard-Thresholding. CoRR abs/1602.06042 (2016) - [i37]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. CoRR abs/1602.06929 (2016) - [i36]Prateek Jain, Nagarajan Natarajan:
Regret Bounds for Non-decomposable Metrics with Missing Labels. CoRR abs/1606.02077 (2016) - [i35]Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain:
Nearly-optimal Robust Matrix Completion. CoRR abs/1606.07315 (2016) - [i34]Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar:
Efficient and Consistent Robust Time Series Analysis. CoRR abs/1607.00146 (2016) - [i33]Kai Zhong, Prateek Jain, Ashish Kapoor:
Fast Second-order Cone Programming for Safe Mission Planning. CoRR abs/1609.05243 (2016) - [i32]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. CoRR abs/1610.03774 (2016) - 2015
- [j6]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval Using Alternating Minimization. IEEE Trans. Signal Process. 63(18): 4814-4826 (2015) - [c47]Kai Zhong, Prateek Jain, Inderjit S. Dhillon:
Efficient Matrix Sensing Using Rank-1 Gaussian Measurements. ALT 2015: 3-18 - [c46]Prateek Jain, Praneeth Netrapalli:
Fast Exact Matrix Completion with Finite Samples. COLT 2015: 1007-1034 - [c45]Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Surrogate Functions for Maximizing Precision at the Top. ICML 2015: 189-198 - [c44]Harikrishna Narasimhan, Purushottam Kar, Prateek Jain:
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes. ICML 2015: 199-208 - [c43]Kush Bhatia, Prateek Jain, Purushottam Kar:
Robust Regression via Hard Thresholding. NIPS 2015: 721-729 - [c42]Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain:
Sparse Local Embeddings for Extreme Multi-label Classification. NIPS 2015: 730-738 - [c41]Prateek Jain, Nagarajan Natarajan, Ambuj Tewari:
Predtron: A Family of Online Algorithms for General Prediction Problems. NIPS 2015: 1009-1017 - [c40]Prateek Jain, Ambuj Tewari:
Alternating Minimization for Regression Problems with Vector-valued Outputs. NIPS 2015: 1126-1134 - [c39]Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi:
Tighter Low-rank Approximation via Sampling the Leveraged Element. SODA 2015: 902-920 - [i31]Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta, Oliver Williams:
To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout. CoRR abs/1503.02031 (2015) - [i30]Harikrishna Narasimhan, Purushottam Kar, Prateek Jain:
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes. CoRR abs/1505.06812 (2015) - [i29]Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Surrogate Functions for Maximizing Precision at the Top. CoRR abs/1505.06813 (2015) - [i28]Kush Bhatia, Prateek Jain, Purushottam Kar:
Robust Regression via Hard Thresholding. CoRR abs/1506.02428 (2015) - [i27]Kush Bhatia, Himanshu Jain, Purushottam Kar, Prateek Jain, Manik Varma:
Locally Non-linear Embeddings for Extreme Multi-label Learning. CoRR abs/1507.02743 (2015) - [i26]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Computing Matrix Squareroot via Non Convex Local Search. CoRR abs/1507.05854 (2015) - [i25]Vidyadhar Rao, Prateek Jain, C. V. Jawahar:
Diverse Yet Efficient Retrieval using Hash Functions. CoRR abs/1509.06553 (2015) - [i24]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
- [j5]Sudheendra Vijayanarasimhan, Prateek Jain, Kristen Grauman:
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning. IEEE Trans. Pattern Anal. Mach. Intell. 36(2): 276-288 (2014) - [c38]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon:
Learning Sparsely Used Overcomplete Dictionaries. COLT 2014: 123-137 - [c37]Prateek Jain, Sewoong Oh:
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions. COLT 2014: 824-856 - [c36]Prateek Jain, Abhradeep Guha Thakurta:
(Near) Dimension Independent Risk Bounds for Differentially Private Learning. ICML 2014: 476-484 - [c35]Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit S. Dhillon:
Large-scale Multi-label Learning with Missing Labels. ICML 2014: 593-601 - [c34]Srinadh Bhojanapalli, Prateek Jain:
Universal Matrix Completion. ICML 2014: 1881-1889 - [c33]Prateek Jain, Ambuj Tewari, Purushottam Kar:
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation. NIPS 2014: 685-693 - [c32]Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions. NIPS 2014: 694-702 - [c31]Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari:
Provable Submodular Minimization using Wolfe's Algorithm. NIPS 2014: 802-809 - [c30]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. NIPS 2014: 1107-1115 - [c29]Prateek Jain, Sewoong Oh:
Provable Tensor Factorization with Missing Data. NIPS 2014: 1431-1439 - [i23]Srinadh Bhojanapalli, Prateek Jain:
Universal Matrix Completion. CoRR abs/1402.2324 (2014) - [i22]Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi:
Tighter Low-rank Approximation via Sampling the Leveraged Element. CoRR abs/1410.3886 (2014) - [i21]Prateek Jain, Ambuj Tewari, Purushottam Kar:
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation. CoRR abs/1410.5137 (2014) - [i20]Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions. CoRR abs/1410.6776 (2014) - [i19]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. CoRR abs/1410.7660 (2014) - [i18]Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari:
Provable Submodular Minimization using Wolfe's Algorithm. CoRR abs/1411.0095 (2014) - [i17]Prateek Jain, Praneeth Netrapalli:
Fast Exact Matrix Completion with Finite Samples. CoRR abs/1411.1087 (2014) - 2013
- [j4]K. S. M. Tozammel Hossain, Debprakash Patnaik, Srivatsan Laxman, Prateek Jain, Chris Bailey-Kellogg, Naren Ramakrishnan:
Improved Multiple Sequence Alignments Using Coupled Pattern Mining. IEEE ACM Trans. Comput. Biol. Bioinform. 10(5): 1098-1112 (2013) - [c28]Prateek Jain, Abhradeep Thakurta:
Differentially Private Learning with Kernels. ICML (3) 2013: 118-126 - [c27]Sivakanth Gopi, Praneeth Netrapalli, Prateek Jain, Aditya V. Nori:
One-Bit Compressed Sensing: Provable Support and Vector Recovery. ICML (3) 2013: 154-162 - [c26]Purushottam Kar, Bharath K. Sriperumbudur, Prateek Jain, Harish Karnick:
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions. ICML (3) 2013: 441-449 - [c25]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval using Alternating Minimization. NIPS 2013: 2796-2804 - [c24]Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain:
Memory Limited, Streaming PCA. NIPS 2013: 2886-2894 - [c23]Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi:
Low-rank matrix completion using alternating minimization. STOC 2013: 665-674 - [c22]Abhirup Nath, Shibnath Mukherjee, Prateek Jain, Navin Goyal, Srivatsan Laxman:
Ad impression forecasting for sponsored search. WWW 2013: 943-952 - [i16]Purushottam Kar, Bharath K. Sriperumbudur, Prateek Jain, Harish Karnick:
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions. CoRR abs/1305.2505 (2013) - [i15]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval using Alternating Minimization. CoRR abs/1306.0160 (2013) - [i14]Prateek Jain, Inderjit S. Dhillon:
Provable Inductive Matrix Completion. CoRR abs/1306.0626 (2013) - [i13]Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain:
Memory Limited, Streaming PCA. CoRR abs/1307.0032 (2013) - [i12]Hsiang-Fu Yu, Prateek Jain, Inderjit S. Dhillon:
Large-scale Multi-label Learning with Missing Labels. CoRR abs/1307.5101 (2013) - [i11]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon:
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. CoRR abs/1310.7991 (2013) - [i10]Prateek Jain, Sewoong Oh:
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions. CoRR abs/1311.2972 (2013) - 2012
- [j3]Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon:
Metric and Kernel Learning Using a Linear Transformation. J. Mach. Learn. Res. 13: 519-547 (2012) - [c21]Prateek Jain, Abhradeep Thakurta:
Mirror Descent Based Database Privacy. APPROX-RANDOM 2012: 579-590 - [c20]K. S. M. Tozammel Hossain, Debprakash Patnaik, Srivatsan Laxman, Prateek Jain, Chris Bailey-Kellogg, Naren Ramakrishnan:
Improved multiple sequence alignments using coupled pattern mining. BCB 2012: 28-35 - [c19]Purushottam Kar, Prateek Jain:
Supervised Learning with Similarity Functions. NIPS 2012: 215-223 - [c18]Ashish Kapoor, Raajay Viswanathan, Prateek Jain:
Multilabel Classification using Bayesian Compressed Sensing. NIPS 2012: 2654-2662 - [c17]Prateek Jain, Pravesh Kothari, Abhradeep Thakurta:
Differentially Private Online Learning. COLT 2012: 24.1-24.34 - [i9]Purushottam Kar, Prateek Jain:
Supervised Learning with Similarity Functions. CoRR abs/1210.5840 (2012) - [i8]Ankan Saha, Prateek Jain, Ambuj Tewari:
The Interplay Between Stability and Regret in Online Learning. CoRR abs/1211.6158 (2012) - [i7]Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi:
Low-rank Matrix Completion using Alternating Minimization. CoRR abs/1212.0467 (2012) - 2011
- [c16]Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Orthogonal Matching Pursuit with Replacement. NIPS 2011: 1215-1223 - [c15]Purushottam Kar, Prateek Jain:
Similarity-based Learning via Data Driven Embeddings. NIPS 2011: 1998-2006 - [i6]Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Orthogonal Matching Pursuit with Replacement. CoRR abs/1106.2774 (2011) - [i5]Prateek Jain, Pravesh Kothari, Abhradeep Thakurta:
Differentially Private Online Learning. CoRR abs/1109.0105 (2011) - [i4]Raajay Viswanathan, Prateek Jain, Srivatsan Laxman, Arvind Arasu:
A Learning Framework for Self-Tuning Histograms. CoRR abs/1111.7295 (2011) - [i3]Purushottam Kar, Prateek Jain:
Similarity-based Learning via Data Driven Embeddings. CoRR abs/1112.5404 (2011) - 2010
- [c14]Sudheendra Vijayanarasimhan, Prateek Jain, Kristen Grauman:
Far-sighted active learning on a budget for image and video recognition. CVPR 2010: 3035-3042 - [c13]Prateek Jain, Sudheendra Vijayanarasimhan, Kristen Grauman:
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning. NIPS 2010: 928-936 - [c12]Prateek Jain, Raghu Meka, Inderjit S. Dhillon:
Guaranteed Rank Minimization via Singular Value Projection. NIPS 2010: 937-945 - [c11]Prateek Jain, Brian Kulis, Inderjit S. Dhillon:
Inductive Regularized Learning of Kernel Functions. NIPS 2010: 946-954
2000 – 2009
- 2009
- [j2]Brian Kulis, Prateek Jain, Kristen Grauman:
Fast Similarity Search for Learned Metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2143-2157 (2009) - [c10]Prateek Jain, Ashish Kapoor:
Active learning for large multi-class problems. CVPR 2009: 762-769 - [c9]Zhengdong Lu, Prateek Jain, Inderjit S. Dhillon:
Geometry-aware metric learning. ICML 2009: 673-680 - [c8]Raghu Meka, Prateek Jain, Inderjit S. Dhillon:
Matrix Completion from Power-Law Distributed Samples. NIPS 2009: 1258-1266 - [i2]Raghu Meka, Prateek Jain, Inderjit S. Dhillon:
Guaranteed Rank Minimization via Singular Value Projection. CoRR abs/0909.5457 (2009) - [i1]Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon:
Metric and Kernel Learning using a Linear Transformation. CoRR abs/0910.5932 (2009) - 2008
- [j1]Prateek Jain, Raghu Meka, Inderjit S. Dhillon:
Simultaneous Unsupervised Learning of Disparate Clusterings. Stat. Anal. Data Min. 1(3): 195-210 (2008) - [c7]Prateek Jain, Brian Kulis, Kristen Grauman:
Fast image search for learned metrics. CVPR 2008 - [c6]Raghu Meka, Prateek Jain, Constantine Caramanis, Inderjit S. Dhillon:
Rank minimization via online learning. ICML 2008: 656-663 - [c5]Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman:
Online Metric Learning and Fast Similarity Search. NIPS 2008: 761-768 - [c4]Prateek Jain, Raghu Meka, Inderjit S. Dhillon:
Simultaneous Unsupervised Learning of Disparate Clusterings. SDM 2008: 858-869 - 2007
- [c3]Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon:
Information-theoretic metric learning. ICML 2007: 209-216 - 2005
- [c2]Prateek Jain, Pabitra Mitra:
Multi-objective Optimization for Adaptive Web Site Generation. PReMI 2005: 654-659 - 2004
- [c1]Prateek Jain, Manav Ratan Mital, Sumit Kumar, Amitabha Mukerjee, Achla M. Raina:
Anaphora Resolution in Multi-Person Dialogues. SIGDIAL Workshop 2004: 47-50
Coauthor Index
[c123] [i105] [i104] [c118] [c114] [c112] [i96] [c107] [c105] [i88] [i85] [i82] [i80] [i78] [j13] [c100] [c99] [c98] [c97] [c94] [i77] [i75] [i74] [i73] [i72] [i67] [c91] [c89] [c88] [i62] [i61] [i58] [c83] [c81] [c80] [c78] [i56] [i54] [i53] [c75] [c74] [c72] [c70] [c68] [i50] [i49] [j9] [c65] [c63] [c62] [i46] [i45] [i41] [j7] [c53] [i37] [i32] [j6] [c46] [i26] [c38] [c30] [i19] [i17] [c27] [c25] [c23] [i15] [i11] [i7]
Abhradeep Thakurta
aka: Abhradeep Guha Thakurta
aka: Abhradeep Guha Thakurta
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load citations from opencitations.net
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OpenAlex data
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last updated on 2025-03-13 21:29 CET by the dblp team
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