Rahul Kidambi - RankMe

44 papers A* 12A 1B 1Misc 1Journal 23Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Inference-time Unlearning Using Conformal Prediction. CoRR Somnath Basu Roy Chowdhury, Rahul Kidambi, Avinava Dubey, David Wang, Gökhan Mergen, Amr Ahmed, Aranyak Mehta
2025	A	`conf`	Fundamental Limits of Perfect Concept Erasure. AISTATS Somnath Basu Roy Chowdhury, Kumar Avinava Dubey, Ahmad Beirami, Rahul Kidambi, Nicholas Monath, Amr Ahmed, Snigdha Chaturvedi
2025	J	`jnl`	Fundamental Limits of Perfect Concept Erasure. CoRR Somnath Basu Roy Chowdhury, Avinava Dubey, Ahmad Beirami, Rahul Kidambi, Nicholas Monath, Amr Ahmed, Snigdha Chaturvedi
2024	A*	`conf`	A Minimaximalist Approach to Reinforcement Learning from Human Feedback. ICML Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal
2024	J	`jnl`	A Minimaximalist Approach to Reinforcement Learning from Human Feedback. CoRR Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal
2024	A*	`conf`	Auctions with LLM Summaries. KDD Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang
2024	J	`jnl`	Auctions with LLM Summaries. CoRR Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang
2024	—	`conf`	Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. EMNLP (Findings) 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
2024	J	`jnl`	Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. CoRR 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
2024	A*	`conf`	Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. ICLR Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Kumar Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
2023	J	`jnl`	Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. CoRR Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
2022	A*	`conf`	Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
2022	J	`jnl`	Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
2021	A*	`conf`	Making Paper Reviewing Robust to Bid Manipulation Attacks. ICML Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger
2021	J	`jnl`	Making Paper Reviewing Robust to Bid Manipulation Attacks. CoRR Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger
2021	A*	`conf`	Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. NeurIPS Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
2021	J	`jnl`	Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. CoRR Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
2021	A*	`conf`	MobILE: Model-Based Imitation Learning From Observation Alone. NeurIPS Rahul Kidambi, Jonathan D. Chang, Wen Sun
2021	J	`jnl`	Optimism is All You Need: Model-Based Imitation Learning From Observation Alone. CoRR Rahul Kidambi, Jonathan D. Chang, Wen Sun
2021	A*	`conf`	Top-k eXtreme Contextual Bandits with Arm Hierarchy. ICML Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean P. Foster, Daniel N. Hill, Inderjit S. Dhillon
2021	J	`jnl`	Top-k eXtreme Contextual Bandits with Arm Hierarchy. CoRR Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean P. Foster, Daniel N. Hill, Inderjit S. Dhillon
2020	B	`conf`	Leverage Score Sampling for Faster Accelerated Regression and ERM. ALT Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
2020	J	`jnl`	MOReL : Model-Based Offline Reinforcement Learning. CoRR Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
2020	A*	`conf`	MOReL: Model-Based Offline Reinforcement Learning. NeurIPS Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
2019	A*	`conf`	Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli
2019	A*	`conf`	The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. NeurIPS Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
2019	J	`jnl`	The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
2018	A*	`conf`	Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
2018	Misc	`conf`	On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. ITA Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
2018	—	`conf`	On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
2018	J	`jnl`	On the insufficiency of existing momentum schemes for Stochastic Optimization. CoRR Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
2017	—	`conf`	A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). FSTTCS Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford
2017	J	`jnl`	A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). CoRR Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford
2017	J	`jnl`	Accelerating Stochastic Gradient Descent. CoRR Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
2017	J	`jnl`	Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles. CoRR Dhruv Mahajan, Vivek Gupta, S. Sathiya Keerthi, Sundararajan Sellamanickam, Shravan Narayanamurthy, Rahul Kidambi
2017	J	`jnl`	Leverage Score Sampling for Faster Accelerated Regression and ERM. CoRR Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
2017	J	`jnl`	Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. J. Mach. Learn. Res. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
2016	J	`jnl`	Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. CoRR Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
2015	—	`conf`	On Shannon capacity and causal estimation. Allerton Rahul Kidambi, Sreeram Kannan
2015	—	`conf`	Submodular Hamming Metrics. NIPS Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes
2015	J	`jnl`	Submodular Hamming Metrics. CoRR Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes
2013	J	`jnl`	A Quantitative Evaluation Framework for Missing Value Imputation Algorithms. CoRR Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke, Vijay Narayanan
2013	J	`jnl`	A Structured Prediction Approach for Missing Value Imputation. CoRR Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi
2012	—	`conf`	Deformable trellises on factor graphs for robust microtubule tracking in clutter. ISBI Rahul Kidambi, Min-Chi Shih, Kenneth Rose