Better Approximate Inference for Partial Likelihood Models with a Latent Structure. - Amrith Setlur; Barnabás Póczos (CoRR, 2019)

Politeness Transfer: A Tag and Generate Approach. - Aman Madaan; Amrith Setlur; Tanmay Parekh; Barnabás Póczos; Graham Neubig; Yiming Yang; Ruslan Salakhutdinov; Alan W. Black; Shrimai Prabhumoye (ACL, 2020)

Robust Handwriting Recognition with Limited and Noisy Data. - Hai Pham; Amrith Setlur; Saket Dingliwal; Tzu-Hsiang Lin; Barnabás Póczos; Kang Huang; Zhuo Li; Jae Lim; Collin McCormack; Tam Vu 002 (ICFHR, 2020)

Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. - Amrith Setlur; Barnabás Póczos; Alan W. Black (INTERSPEECH, 2020)

ReStGAN: A step towards visually guided shopper experience via text-to-image synthesis. - Shiv Surya; Amrith Setlur; Arijit Biswas; Sumit Negi (WACV, 2020)

Covariate Distribution Aware Meta-learning. - Amrith Setlur; Saket Dingliwal; Barnabás Póczos (CoRR, 2020)

Is Support Set Diversity Necessary for Meta-Learning? - Amrith Setlur; Oscar Li; Virginia Smith (CoRR, 2020)

Towards Using Heterogeneous Relation Graphs for End-to-End TTS. - Amrith Setlur; Aman Madaan; Tanmay Parekh; Yiming Yang; Alan W. Black (ASRU, 2021)

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

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

Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods. - Amrith Setlur; Oscar Li; Virginia Smith (CoRR, 2021)

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. - Amrith Setlur; Benjamin Eysenbach; Virginia Smith; Sergey Levine (NeurIPS, 2022)

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

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

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

Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (CoRR, 2023)

Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (CoRR, 2023)

Leveraging Public Representations for Private Transfer Learning. - Pratiksha Thaker; Amrith Setlur; Zhiwei Steven Wu; Virginia Smith (CoRR, 2023)

Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (ICLR, 2024)

Deep Neural Networks Tend To Extrapolate Predictably. - Katie Kang; Amrith Setlur; Claire J. Tomlin; Sergey Levine (ICLR, 2024)

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

Private and Personalized Frequency Estimation in a Federated Setting. - Amrith Setlur; Vitaly Feldman; Kunal Talwar (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 (NeurIPS, 2024)

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift. - Pratiksha Thaker; Amrith Setlur; Steven Z. Wu; Virginia Smith (NeurIPS, 2024)

What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? - Katie Kang; Amrith Setlur; Dibya Ghosh; Jacob Steinhardt; Claire J. Tomlin; Sergey Levine; Aviral Kumar (CoRR, 2024)

Multitask Learning Can Improve Worst-Group Outcomes. - Atharva Kulkarni; Lucio M. Dery; Amrith Setlur; Aditi Raghunathan; Ameet Talwalkar; Graham Neubig (Trans. Mach. Learn. Res., 2024)

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 (ICLR, 2025)

Scaling Test-Time Compute Without Verification or RL is Suboptimal. - Amrith Setlur; Nived Rajaraman; Sergey Levine; Aviral Kumar (CoRR, 2025)

Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning. - Yuxiao Qu; Matthew Y. R. Yang; Amrith Setlur; Lewis Tunstall; Edward Emanuel Beeching; Ruslan Salakhutdinov; Aviral Kumar (CoRR, 2025)

Exact Unlearning of Finetuning Data via Model Merging at Scale. - Kevin Kuo; Amrith Setlur; Kartik Srinivas; Aditi Raghunathan; Virginia Smith (CoRR, 2025)

Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction. - Junhong Shen; Hao Bai; Lunjun Zhang; Yifei Zhou; Amrith Setlur; Shengbang Tong; Diego Caples; Nan Jiang 008; Tong Zhang 001; Ameet Talwalkar; Aviral Kumar (CoRR, 2025)

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs. - Amrith Setlur; Matthew Y. R. Yang; Charlie Snell; Jeremy Greer; Ian Wu; Virginia Smith; Max Simchowitz; Aviral Kumar (CoRR, 2025)

Lower Bounds for Public-Private Learning under Distribution Shift. - Amrith Setlur; Pratiksha Thaker; Jonathan Ullman (CoRR, 2025)