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ORCIDRahul Kidambi
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2020 – today
- 2024
[c20]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:
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. EMNLP (Findings) 2024: 2153-2186- [c19]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 - [c18]Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal:
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. ICML 2024 - [c17]Avinava Dubey
, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang:
Auctions with LLM Summaries. KDD 2024: 713-722 - [i20]Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal:
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. CoRR abs/2401.04056 (2024) - [i19]Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang:
Auctions with LLM Summaries. CoRR abs/2404.08126 (2024) - [i18]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 abs/2407.15762 (2024) - 2023
- [i17]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 abs/2310.11401 (2023) - 2022
- [c16]Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon:
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR 2022: 791-802 - [i16]Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon:
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR abs/2204.10936 (2022) - 2021
- [c15]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. ICML 2021: 9422-9433 - [c14]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: 11240-11250 - [c13]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: 965-979 - [c12]Rahul Kidambi, Jonathan D. Chang, Wen Sun:
MobILE: Model-Based Imitation Learning From Observation Alone. NeurIPS 2021: 28598-28611 - [i15]Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger:
Making Paper Reviewing Robust to Bid Manipulation Attacks. CoRR abs/2102.06020 (2021) - [i14]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 abs/2102.07800 (2021) - [i13]Rahul Kidambi, Jonathan D. Chang, Wen Sun:
Optimism is All You Need: Model-Based Imitation Learning From Observation Alone. CoRR abs/2102.10769 (2021) - [i12]Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun:
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. CoRR abs/2106.03207 (2021) - 2020
- [c11]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: 22-47 - [c10]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS 2020 - [i11]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL : Model-Based Offline Reinforcement Learning. CoRR abs/2005.05951 (2020)
2010 – 2019
- 2019
- [c9]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: 3190-3193 - [c8]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: 14951-14962 - [i10]Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli:
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR abs/1904.12838 (2019) - 2018
- [c7]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT 2018: 545-604 - [c6]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR 2018 - [c5]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. ITA 2018: 1-9 - [i9]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. CoRR abs/1803.05591 (2018) - 2017
- [j1]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: 223:1-223:42 (2017) - [c4]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: 2:1-2:10 - [i8]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent. CoRR abs/1704.08227 (2017) - [i7]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 abs/1710.09430 (2017) - [i6]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 abs/1711.05482 (2017) - [i5]Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford:
Leverage Score Sampling for Faster Accelerated Regression and ERM. CoRR abs/1711.08426 (2017) - 2016
- [i4]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. CoRR abs/1610.03774 (2016) - 2015
- [c3]Rahul Kidambi, Sreeram Kannan:
On Shannon capacity and causal estimation. Allerton 2015: 988-992 - [c2]Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes:
Submodular Hamming Metrics. NIPS 2015: 3141-3149 - [i3]Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes:
Submodular Hamming Metrics. CoRR abs/1511.02163 (2015) - 2013
- [i2]Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi:
A Structured Prediction Approach for Missing Value Imputation. CoRR abs/1311.2137 (2013) - [i1]Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke, Vijay Narayanan:
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms. CoRR abs/1311.2276 (2013) - 2012
- [c1]Rahul Kidambi, Min-Chi Shih, Kenneth Rose:
Deformable trellises on factor graphs for robust microtubule tracking in clutter. ISBI 2012: 676-679
Coauthor Index
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