Rahul Kidambi

According to our database, Rahul Kidambi authored at least 29 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

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, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent

CoRR, 2024

Auctions with LLM Summaries.

Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

A Minimaximalist Approach to Reinforcement Learning from Human Feedback.

Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Twelfth International Conference on Learning Representations, 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 Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

2022

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion.

Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon

Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

2021

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

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

CoRR, 2021

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

Rahul Kidambi, Jonathan D. Chang, Wen Sun

CoRR, 2021

MobILE: Model-Based Imitation Learning From Observation Alone.

Rahul Kidambi, Jonathan D. Chang, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Making Paper Reviewing Robust to Bid Manipulation Attacks.

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

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

MOReL: Model-Based Offline Reinforcement Learning.

Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Leverage Score Sampling for Faster Accelerated Regression and ERM.

Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford

Proceedings of the Algorithmic Learning Theory, 2020

2019

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

Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Open Problem: Do Good Algorithms Necessarily Query Bad Points?

Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli

Proceedings of the Conference on Learning Theory, 2019

2018

On the insufficiency of existing momentum schemes for Stochastic Optimization.

Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade

Proceedings of the 6th International Conference on Learning Representations, 2018

Accelerating Stochastic Gradient Descent for Least Squares Regression.

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

Proceedings of the Conference On Learning Theory, 2018

2017

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification.

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

J. Mach. Learn. Res., 2017

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

Dhruv Mahajan, Vivek Gupta, S. Sathiya Keerthi, Sundararajan Sellamanickam, Shravan Narayanamurthy, Rahul Kidambi

CoRR, 2017

Accelerating Stochastic Gradient Descent.

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

CoRR, 2017

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

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

Proceedings of the 37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, 2017

2016

Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging.

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

CoRR, 2016

2015

Submodular Hamming Metrics.

Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

On Shannon capacity and causal estimation.

Rahul Kidambi, Sreeram Kannan

Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

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

A Structured Prediction Approach for Missing Value Imputation.

Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi

CoRR, 2013

2012

Deformable trellises on factor graphs for robust microtubule tracking in clutter.

Rahul Kidambi, Min-Chi Shih, Kenneth Rose

Proceedings of the 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2012