Amrith Setlur

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 32

Top Topics

Auxiliary Variables

Latent Structure

Transfer Learning

Top Venues

Publications

Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
Multitask Learning Can Improve Worst-Group Outcomes. Trans. Mach. Learn. Res. 2024 (2024)
Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn
Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features. ICLR (2024)
Katie Kang, Amrith Setlur, Claire J. Tomlin, Sergey Levine
Deep Neural Networks Tend To Extrapolate Predictably. ICLR (2024)
Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. CoRR (2024)
Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. ICLR (2023)
Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
Multitask Learning Can Improve Worst-Group Outcomes. CoRR (2023)
Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts. CoRR (2023)
Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith
Leveraging Public Representations for Private Transfer Learning. CoRR (2023)
Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
Contextual Reliability: When Different Features Matter in Different Contexts. ICML (2023)
Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS (2023)
Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. CoRR (2023)
Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features. CoRR (2023)
Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR (2023)
Gaurav R. Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
Contextual Reliability: When Different Features Matter in Different Contexts. CoRR (2023)
Katie Kang, Amrith Setlur, Claire J. Tomlin, Sergey Levine
Deep Neural Networks Tend To Extrapolate Predictably. CoRR (2023)
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. NeurIPS (2022)
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. CoRR (2022)
Amrith Setlur, Oscar Li, Virginia Smith
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. NeurIPS (2021)
Amrith Setlur, Oscar Li, Virginia Smith
Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods. CoRR (2021)
Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, Zachary Chase Lipton
Explaining the Efficacy of Counterfactually Augmented Data. ICLR (2021)
Amrith Setlur, Aman Madaan, Tanmay Parekh, Yiming Yang, Alan W. Black
Towards Using Heterogeneous Relation Graphs for End-to-End TTS. ASRU (2021)
Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, Zachary C. Lipton
Explaining The Efficacy of Counterfactually-Augmented Data. CoRR (2020)
Amrith Setlur, Saket Dingliwal, Barnabás Póczos
Covariate Distribution Aware Meta-learning. CoRR (2020)
Amrith Setlur, Oscar Li, Virginia Smith
Is Support Set Diversity Necessary for Meta-Learning? CoRR (2020)
Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabás Póczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye
Politeness Transfer: A Tag and Generate Approach. ACL (2020)
Shiv Surya, Amrith Setlur, Arijit Biswas, Sumit Negi
ReStGAN: A step towards visually guided shopper experience via text-to-image synthesis. WACV (2020)
Amrith Setlur, Barnabás Póczos, Alan W. Black
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. INTERSPEECH (2020)
Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabás Póczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu
Robust Handwriting Recognition with Limited and Noisy Data. ICFHR (2020)
Amrith Setlur, Barnabás Póczos, Alan W. Black
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. CoRR (2020)
Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabás Póczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye
Politeness Transfer: A Tag and Generate Approach. CoRR (2020)
Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabás Póczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu
Robust Handwriting Recognition with Limited and Noisy Data. CoRR (2020)
Amrith Setlur, Barnabás Póczos
Better Approximate Inference for Partial Likelihood Models with a Latent Structure. CoRR (2019)