Deformable trellises on factor graphs for robust microtubule tracking in clutter. - Rahul Kidambi; Min-Chi Shih; Kenneth Rose (ISBI, 2012)

A Structured Prediction Approach for Missing Value Imputation. - Rahul Kidambi; Vinod Nair; Sundararajan Sellamanickam; S. Sathiya Keerthi (CoRR, 2013)

A Quantitative Evaluation Framework for Missing Value Imputation Algorithms. - Vinod Nair; Rahul Kidambi; Sundararajan Sellamanickam; S. Sathiya Keerthi; Johannes Gehrke; Vijay Narayanan (CoRR, 2013)

On Shannon capacity and causal estimation. - Rahul Kidambi; Sreeram Kannan (Allerton, 2015)

Submodular Hamming Metrics. - Jennifer Gillenwater; Rishabh K. Iyer; Bethany Lusch; Rahul Kidambi; Jeff A. Bilmes (NIPS, 2015)

Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (CoRR, 2016)

A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Venkata Krishna Pillutla; Aaron Sidford (FSTTCS, 2017)

Accelerating Stochastic Gradient Descent. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (CoRR, 2017)

Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles. - Dhruv Mahajan 001; Vivek Gupta 001; S. Sathiya Keerthi; Sundararajan Sellamanickam; Shravan Narayanamurthy; Rahul Kidambi (CoRR, 2017)

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (J. Mach. Learn. Res., 2017)

Accelerating Stochastic Gradient Descent for Least Squares Regression. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (COLT, 2018)

On the insufficiency of existing momentum schemes for Stochastic Optimization. - Rahul Kidambi; Praneeth Netrapalli; Prateek Jain 002; Sham M. Kakade (ICLR, 2018)

Open Problem: Do Good Algorithms Necessarily Query Bad Points? - Rong Ge 001; Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Dheeraj M. Nagaraj; Praneeth Netrapalli (COLT, 2019)

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. - Rong Ge 001; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli (NeurIPS, 2019)

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. - Rong Ge 001; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli (CoRR, 2019)

Leverage Score Sampling for Faster Accelerated Regression and ERM. - Naman Agarwal; Sham M. Kakade; Rahul Kidambi; Yin Tat Lee; Praneeth Netrapalli; Aaron Sidford (ALT, 2020)

MOReL: Model-Based Offline Reinforcement Learning. - Rahul Kidambi; Aravind Rajeswaran; Praneeth Netrapalli; Thorsten Joachims (NeurIPS, 2020)

Top-k eXtreme Contextual Bandits with Arm Hierarchy. - Rajat Sen; Alexander Rakhlin; Lexing Ying; Rahul Kidambi; Dean P. Foster; Daniel N. Hill; Inderjit S. Dhillon (ICML, 2021)

Making Paper Reviewing Robust to Bid Manipulation Attacks. - Ruihan Wu; Chuan Guo 001; Felix Wu; Rahul Kidambi; Laurens van der Maaten; Kilian Q. Weinberger (ICML, 2021)

Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. - Jonathan D. Chang; Masatoshi Uehara; Dhruv Sreenivas; Rahul Kidambi; Wen Sun 002 (NeurIPS, 2021)

MobILE: Model-Based Imitation Learning From Observation Alone. - Rahul Kidambi; Jonathan D. Chang; Wen Sun 002 (NeurIPS, 2021)

Optimism is All You Need: Model-Based Imitation Learning From Observation Alone. - Rahul Kidambi; Jonathan D. Chang; Wen Sun 002 (CoRR, 2021)

Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. - Jonathan D. Chang; Masatoshi Uehara; Dhruv Sreenivas; Rahul Kidambi; Wen Sun 002 (CoRR, 2021)

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. - Adam Block; Rahul Kidambi; Daniel N. Hill; Thorsten Joachims; Inderjit S. Dhillon (SIGIR, 2022)

Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (EMNLP (Findings), 2024)

Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. - Somnath Basu Roy Chowdhury; Nicholas Monath; Ahmad Beirami; Rahul Kidambi; Kumar Avinava Dubey; Amr Ahmed 001; Snigdha Chaturvedi (ICLR, 2024)

A Minimaximalist Approach to Reinforcement Learning from Human Feedback. - Gokul Swamy; Christoph Dann; Rahul Kidambi; Steven Wu 001; Alekh Agarwal (ICML, 2024)

Auctions with LLM Summaries. - Avinava Dubey; Zhe Feng 004; Rahul Kidambi; Aranyak Mehta; Di Wang 005 (KDD, 2024)

Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (CoRR, 2024)

Fundamental Limits of Perfect Concept Erasure. - Somnath Basu Roy Chowdhury; Kumar Avinava Dubey; Ahmad Beirami; Rahul Kidambi; Nicholas Monath; Amr Ahmed 001; Snigdha Chaturvedi (AISTATS, 2025)