Amrith Setlur - RankMe

54 papers A* 17A 2B 1C 1Journal 33

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning. CoRR Matthew Y. R. Yang, Hao Bai, Ian Wu, Gene Yang, Amrith Setlur, Aviral Kumar
2026	J	`jnl`	IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL. CoRR Zhoujun Cheng, Yutao Xie, Yuxiao Qu, Amrith Setlur, Shibo Hao, Varad Pimpalkhute, Tongtong Liang, Feng Yao, Zhengzhong Liu, Eric P. Xing, Virginia Smith, Ruslan Salakhutdinov, Zhiting Hu, Taylor W. Killian, Aviral Kumar
2026	J	`jnl`	POPE: Learning to Reason on Hard Problems via Privileged On-Policy Exploration. CoRR Yuxiao Qu, Amrith Setlur, Virginia Smith, Ruslan Salakhutdinov, Aviral Kumar
2026	J	`jnl`	Reasoning Cache: Continual Improvement Over Long Horizons via Short-Horizon RL. CoRR Ian Wu, Yuxiao Qu, Amrith Setlur, Aviral Kumar
2026	J	`jnl`	Reuse your FLOPs: Scaling RL on Hard Problems by Conditioning on Very Off-Policy Prefixes. CoRR Amrith Setlur, Zijian Wang, Andrew Cohen, Paria Rashidinejad, Sang Michael Xie
2025	J	`jnl`	Exact Unlearning of Finetuning Data via Model Merging at Scale. CoRR Kevin Kuo, Amrith Setlur, Kartik Srinivas, Aditi Raghunathan, Virginia Smith
2025	J	`jnl`	Lower Bounds for Public-Private Learning under Distribution Shift. CoRR Amrith Setlur, Pratiksha Thaker, Jonathan Ullman
2025	J	`jnl`	Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning. CoRR Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, Aviral Kumar
2025	A*	`conf`	Optimizing Test-Time Compute via Meta Reinforcement Finetuning. ICML Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, Aviral Kumar
2025	J	`jnl`	RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems. CoRR Yuxiao Qu, Anikait Singh, Yoonho Lee, Amrith Setlur, Ruslan Salakhutdinov, Chelsea Finn, Aviral Kumar
2025	A*	`conf`	Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. ICLR Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar
2025	A*	`conf`	Scaling Test-Time Compute Without Verification or RL is Suboptimal. ICML Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar
2025	J	`jnl`	Scaling Test-Time Compute Without Verification or RL is Suboptimal. CoRR Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar
2025	J	`jnl`	Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction. CoRR Junhong Shen, Hao Bai, Lunjun Zhang, Yifei Zhou, Amrith Setlur, Shengbang Tong, Diego Caples, Nan Jiang, Tong Zhang, Ameet Talwalkar, Aviral Kumar
2025	A*	`conf`	What Do Learning Dynamics Reveal About Generalization in LLM Mathematical Reasoning? ICML Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt, Claire J. Tomlin, Sergey Levine, Aviral Kumar
2025	J	`jnl`	e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs. CoRR Amrith Setlur, Matthew Y. R. Yang, Charlie Snell, Jeremy Greer, Ian Wu, Virginia Smith, Max Simchowitz, Aviral Kumar
2024	A*	`conf`	Deep Neural Networks Tend To Extrapolate Predictably. ICLR Katie Kang, Amrith Setlur, Claire J. Tomlin, Sergey Levine
2024	J	`jnl`	Multitask Learning Can Improve Worst-Group Outcomes. Trans. Mach. Learn. Res. Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
2024	A*	`conf`	On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift. NeurIPS Pratiksha Thaker, Amrith Setlur, Steven Z. Wu, Virginia Smith
2024	A*	`conf`	Private and Personalized Frequency Estimation in a Federated Setting. NeurIPS Amrith Setlur, Vitaly Feldman, Kunal Talwar
2024	A*	`conf`	Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features. ICLR Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn
2024	A*	`conf`	Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models. ICML Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine
2024	A*	`conf`	RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. NeurIPS Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar
2024	J	`jnl`	RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. CoRR Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar
2024	J	`jnl`	Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. CoRR Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar
2024	J	`jnl`	What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? CoRR Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt, Claire J. Tomlin, Sergey Levine, Aviral Kumar
2023	A*	`conf`	Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. ICLR Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
2023	J	`jnl`	Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. CoRR Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
2023	A*	`conf`	Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
2023	J	`jnl`	Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
2023	J	`jnl`	Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts. CoRR Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn
2023	A*	`conf`	Contextual Reliability: When Different Features Matter in Different Contexts. ICML Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
2023	J	`jnl`	Contextual Reliability: When Different Features Matter in Different Contexts. CoRR Gaurav R. Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
2023	J	`jnl`	Deep Neural Networks Tend To Extrapolate Predictably. CoRR Katie Kang, Amrith Setlur, Claire J. Tomlin, Sergey Levine
2023	J	`jnl`	Leveraging Public Representations for Private Transfer Learning. CoRR Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith
2023	J	`jnl`	Multitask Learning Can Improve Worst-Group Outcomes. CoRR Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
2023	J	`jnl`	Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features. CoRR Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn
2022	A*	`conf`	Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. NeurIPS Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
2022	J	`jnl`	Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. CoRR Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
2021	A*	`conf`	Explaining the Efficacy of Counterfactually Augmented Data. ICLR Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, Zachary Chase Lipton
2021	J	`jnl`	Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods. CoRR Amrith Setlur, Oscar Li, Virginia Smith
2021	C	`conf`	Towards Using Heterogeneous Relation Graphs for End-to-End TTS. ASRU Amrith Setlur, Aman Madaan, Tanmay Parekh, Yiming Yang, Alan W. Black
2021	A*	`conf`	Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. NeurIPS Amrith Setlur, Oscar Li, Virginia Smith
2020	J	`jnl`	Covariate Distribution Aware Meta-learning. CoRR Amrith Setlur, Saket Dingliwal, Barnabás Póczos
2020	J	`jnl`	Explaining The Efficacy of Counterfactually-Augmented Data. CoRR Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, Zachary C. Lipton
2020	J	`jnl`	Is Support Set Diversity Necessary for Meta-Learning? CoRR Amrith Setlur, Oscar Li, Virginia Smith
2020	A	`conf`	Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. INTERSPEECH Amrith Setlur, Barnabás Póczos, Alan W. Black
2020	J	`jnl`	Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. CoRR Amrith Setlur, Barnabás Póczos, Alan W. Black
2020	A*	`conf`	Politeness Transfer: A Tag and Generate Approach. ACL Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabás Póczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye
2020	J	`jnl`	Politeness Transfer: A Tag and Generate Approach. CoRR Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabás Póczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye
2020	A	`conf`	ReStGAN: A step towards visually guided shopper experience via text-to-image synthesis. WACV Shiv Surya, Amrith Setlur, Arijit Biswas, Sumit Negi
2020	B	`conf`	Robust Handwriting Recognition with Limited and Noisy Data. ICFHR Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabás Póczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu
2020	J	`jnl`	Robust Handwriting Recognition with Limited and Noisy Data. CoRR Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabás Póczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu
2019	J	`jnl`	Better Approximate Inference for Partial Likelihood Models with a Latent Structure. CoRR Amrith Setlur, Barnabás Póczos