Rahul Kidambi

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 33

Top Topics

Stochastic Gradient Descent

Imitation Learning

Top Venues

Publications

Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang
Auctions with LLM Summaries. KDD (2024)
Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. CoRR (2024)
Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang
Auctions with LLM Summaries. CoRR (2024)
Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Kumar Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. ICLR (2024)
Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent
Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. CoRR (2024)
Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. CoRR (2023)
Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR (2022)
Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR (2022)
Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger
Making Paper Reviewing Robust to Bid Manipulation Attacks. CoRR (2021)
Rahul Kidambi, Jonathan D. Chang, Wen Sun
Optimism is All You Need: Model-Based Imitation Learning From Observation Alone. CoRR (2021)
Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger
Making Paper Reviewing Robust to Bid Manipulation Attacks. ICML (2021)
Rahul Kidambi, Jonathan Chang, Wen Sun
MobILE: Model-Based Imitation Learning From Observation Alone. NeurIPS (2021)
Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. CoRR (2021)
Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. NeurIPS (2021)
Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean P. Foster, Daniel N. Hill, Inderjit S. Dhillon
Top-k eXtreme Contextual Bandits with Arm Hierarchy. CoRR (2021)
Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel N. Hill, Inderjit S. Dhillon
Top-k eXtreme Contextual Bandits with Arm Hierarchy. ICML (2021)
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS (2020)
Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
Leverage Score Sampling for Faster Accelerated Regression and ERM. ALT (2020)
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
MOReL : Model-Based Offline Reinforcement Learning. CoRR (2020)
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)
Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. NeurIPS (2019)
Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR (2019)
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR (2018)
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT (2018)
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
On the insufficiency of existing momentum schemes for Stochastic Optimization. CoRR (2018)
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. ITA (2018)
Dhruv Mahajan, Vivek Gupta, S. Sathiya Keerthi, Sundararajan Sellamanickam, Shravan Narayanamurthy, Rahul Kidambi
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles. CoRR (2017)
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)
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 (2017)
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 (2017)
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
Accelerating Stochastic Gradient Descent. CoRR (2017)
Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
Leverage Score Sampling for Faster Accelerated Regression and ERM. CoRR (2017)
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. CoRR (2016)
Rahul Kidambi, Sreeram Kannan
On Shannon capacity and causal estimation. Allerton (2015)
Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes
Submodular Hamming Metrics. NIPS (2015)
Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes
Submodular Hamming Metrics. CoRR (2015)
Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi
A Structured Prediction Approach for Missing Value Imputation. CoRR (2013)
Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke, Vijay Narayanan
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms. CoRR (2013)
Rahul Kidambi, Min-Chi Shih, Kenneth Rose
Deformable trellises on factor graphs for robust microtubule tracking in clutter. ISBI (2012)