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Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage


Jun 14, 2021
Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun

* 42 pages, 5 figures, 7 tables 

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Making Paper Reviewing Robust to Bid Manipulation Attacks


Feb 22, 2021
Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger


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Optimism is All You Need: Model-Based Imitation Learning From Observation Alone


Feb 22, 2021
Rahul Kidambi, Jonathan Chang, Wen Sun

* 25 pages, 3 figures, 1 tabular column 

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Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy


Feb 15, 2021
Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel Hill, Inderjit Dhillon


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MOReL : Model-Based Offline Reinforcement Learning


May 12, 2020
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

* First two authors contributed equally. 18 pages of main text. 2 sections of appendix 

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The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure


Apr 29, 2019
Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli

* 25 pages, 5 tables, 5 figures 

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On the insufficiency of existing momentum schemes for Stochastic Optimization


Jul 31, 2018
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade

* 28 pages, 10 figures. Updated acknowledgements. Appeared as an oral presentation at International Conference on Learning Representations (ICLR), 2018. Code implementing the ASGD method can be found at https://github.com/rahulkidambi/AccSGD 

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Accelerating Stochastic Gradient Descent For Least Squares Regression


Jul 31, 2018
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

* 54 pages, 3 figures, 1 table; updated acknowledgements, minor title change. Paper appeared in the proceedings of the Conference on Learning Theory (COLT), 2018 

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Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification


Jul 31, 2018
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

* 39 pages. Published in the Journal of Machine Learning Research (JMLR) 

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A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)


Jul 21, 2018
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford

* Lemma 1 has been updated in v2 

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Leverage Score Sampling for Faster Accelerated Regression and ERM


Nov 22, 2017
Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford


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Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles


Nov 15, 2017
Dhruv Mahajan, Vivek Gupta, S Sathiya Keerthi, Sellamanickam Sundararajan, Shravan Narayanamurthy, Rahul Kidambi

* 12 Pages, 4 Figures, 12 Pages, Under Review in SDM 2018 

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Submodular Hamming Metrics


Nov 06, 2015
Jennifer Gillenwater, Rishabh Iyer, Bethany Lusch, Rahul Kidambi, Jeff Bilmes

* 15 pages, 1 figure, a short version of this will appear in the NIPS 2015 conference 

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A Quantitative Evaluation Framework for Missing Value Imputation Algorithms


Nov 10, 2013
Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke, Vijay Narayanan

* 9 pages 

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A Structured Prediction Approach for Missing Value Imputation


Nov 09, 2013
Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi

* 9 Pages 

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