Kidambi, Rahul

16 publications

NeurIPS 2025 EUGens: Efficient, Unified and General Dense Layers Sang Min Kim, Byeongchan Kim, Arijit Sehanobish, Somnath Basu Roy Chowdhury, Rahul Kidambi, Dongseok Shim, Kumar Avinava Dubey, Snigdha Chaturvedi, Min-hwan Oh, Krzysztof Marcin Choromanski

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

ICML 2024 A Minimaximalist Approach to Reinforcement Learning from Human Feedback Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal

NeurIPSW 2024 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, Kumar Avinava Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent

ICLR 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, Snigdha Chaturvedi

ICML 2021 Making Paper Reviewing Robust to Bid Manipulation Attacks Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens Van Der Maaten, Kilian Weinberger

NeurIPS 2021 Mitigating Covariate Shift in Imitation Learning via Offline Data with Partial Coverage Jonathan Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun

NeurIPS 2021 MobILE: Model-Based Imitation Learning from Observation Alone Rahul Kidambi, Jonathan Chang, Wen Sun

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

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

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

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

NeurIPS 2019 The Step Decay Schedule: A near Optimal, Geometrically Decaying Learning Rate Procedure for Least Squares Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli

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

ICLR 2018 On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade

NeurIPS 2015 Submodular Hamming Metrics Jennifer A Gillenwater, Rishabh K Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes