Setlur, Amrith

30 publications

ICLRW 2025 Exact Unlearning of Finetuning Data via Model Merging at Scale Kevin Kuo, Amrith Setlur, Kartik Srinivas, Aditi Raghunathan, Virginia Smith

ICML 2025 Optimizing Test-Time Compute via Meta Reinforcement Finetuning Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, Aviral Kumar

ICLRW 2025 Optimizing Test-Time Compute via Meta Reinforcement Finetuning Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, Aviral Kumar

ICLR 2025 Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar

ICML 2025 Scaling Test-Time Compute Without Verification or RL Is Suboptimal Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar

ICLRW 2025 Scaling Test-Time Compute Without Verification or RL Is Suboptimal Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar

NeurIPS 2025 Thinking vs. Doing: Improving Agent Reasoning by Scaling Test-Time Interaction Junhong Shen, Hao Bai, Lunjun Zhang, Yifei Zhou, Amrith Setlur, Shengbang Tong, Diego Caples, Nan Jiang, Tong Zhang, Ameet Talwalkar, Aviral Kumar

ICML 2025 What Do Learning Dynamics Reveal About Generalization in LLM Mathematical Reasoning? Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt, Claire Tomlin, Sergey Levine, Aviral Kumar

ICLR 2024 Deep Neural Networks Tend to Extrapolate Predictably Katie Kang, Amrith Setlur, Claire Tomlin, Sergey Levine

ICMLW 2024 Learning to Reason by Failing: Offline RL on Sub-Optimal Rollouts Scales Synthetic Data by 8x Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar

TMLR 2024 Multitask Learning Can Improve Worst-Group Outcomes Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig

NeurIPS 2024 On the Benefits of Public Representations for Private Transfer Learning Under Distribution Shift Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith

NeurIPS 2024 Private and Personalized Frequency Estimation in a Federated Setting Amrith Setlur, Vitaly Feldman, Kunal Talwar

ICLR 2024 Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features Annie S Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn

ICML 2024 Prompting Is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine

ICLRW 2024 Prompting for Robustness: Extracting Robust Classifiers from Foundation Models Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine

NeurIPS 2024 RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar

ICLR 2023 Bitrate-Constrained DRO: Beyond Worst Case Robustness to Unknown Group Shifts Amrith Setlur, Don Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine

NeurIPS 2023 Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift Saurabh Garg, Amrith Setlur, Zachary Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan

NeurIPSW 2023 Confidence-Based Model Selection: When to Take Shortcuts in Spurious Settings Annie S Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn

ICML 2023 Contextual Reliability: When Different Features Matter in Different Contexts Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca Dragan, Aditi Raghunathan

NeurIPSW 2023 Private and Personalized Histogram Estimation in a Federated Setting Amrith Setlur, Vitaly Feldman, Kunal Talwar

ICLRW 2023 Project with Source, Probe with Target: Extracting Useful Features for Adaptation to Distribution Shifts Annie S Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn

NeurIPS 2022 Adversarial Unlearning: Reducing Confidence Along Adversarial Directions Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine

NeurIPSW 2022 Bitrate-Constrained DRO: Beyond Worst Case Robustness to Unknown Group Shifts Amrith Setlur, Don Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine

ICLRW 2022 Maximizing Entropy on Adversarial Examples Can Improve Generalization Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine

ICLR 2021 Explaining the Efficacy of Counterfactually Augmented Data Divyansh Kaushik, Amrith Setlur, Eduard H Hovy, Zachary Chase Lipton

NeurIPS 2021 Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution Amrith Setlur, Oscar Li, Virginia Smith

ICMLW 2020 Covariate Distribution Aware Meta-Learning Amrith Setlur, Saket Dingliwal, Barnabas Poczos

WACV 2020 ReStGAN: A Step Towards Visually Guided Shopper Experience via Text-to-Image Synthesis Shiv Surya, Amrith Setlur, Arijit Biswas, Sumit Negi