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ORCIDAmrith Setlur
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2020 – today
- 2024
[j1]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)- [c14]Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn:
Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features. ICLR 2024 - [c13]Katie Kang, Amrith Setlur, Claire J. Tomlin, Sergey Levine:
Deep Neural Networks Tend To Extrapolate Predictably. ICLR 2024 - [c12]Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine:
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models. ICML 2024 - [i20]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 abs/2406.14532 (2024) - [i19]Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar:
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. CoRR abs/2410.08146 (2024) - [i18]Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt, Claire J. Tomlin, Sergey Levine, Aviral Kumar:
What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? CoRR abs/2411.07681 (2024) - 2023
- [c11]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 - [c10]Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan:
Contextual Reliability: When Different Features Matter in Different Contexts. ICML 2023: 11300-11320 - [c9]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 - [i17]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 abs/2302.02931 (2023) - [i16]Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn:
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features. CoRR abs/2302.05441 (2023) - [i15]Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn:
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts. CoRR abs/2306.11120 (2023) - [i14]Gaurav R. Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan:
Contextual Reliability: When Different Features Matter in Different Contexts. CoRR abs/2307.10026 (2023) - [i13]Katie Kang, Amrith Setlur, Claire J. Tomlin, Sergey Levine:
Deep Neural Networks Tend To Extrapolate Predictably. CoRR abs/2310.00873 (2023) - [i12]Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig:
Multitask Learning Can Improve Worst-Group Outcomes. CoRR abs/2312.03151 (2023) - [i11]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 abs/2312.03318 (2023) - [i10]Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith:
Leveraging Public Representations for Private Transfer Learning. CoRR abs/2312.15551 (2023) - 2022
- [c8]Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine:
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. NeurIPS 2022 - [i9]Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine:
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. CoRR abs/2206.01367 (2022) - 2021
- [c7]Amrith Setlur, Aman Madaan, Tanmay Parekh, Yiming Yang, Alan W. Black:
Towards Using Heterogeneous Relation Graphs for End-to-End TTS. ASRU 2021: 1162-1169 - [c6]Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, Zachary Chase Lipton:
Explaining the Efficacy of Counterfactually Augmented Data. ICLR 2021 - [c5]Amrith Setlur, Oscar Li, Virginia Smith:
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. NeurIPS 2021: 3770-3783 - [i8]Amrith Setlur, Oscar Li, Virginia Smith:
Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods. CoRR abs/2102.11503 (2021) - 2020
- [c4]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: 1869-1881 - [c3]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: 301-306 - [c2]Amrith Setlur, Barnabás Póczos, Alan W. Black:
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. INTERSPEECH 2020: 180-184 - [c1]Shiv Surya, Amrith Setlur, Arijit Biswas, Sumit Negi:
ReStGAN: A step towards visually guided shopper experience via text-to-image synthesis. WACV 2020: 1189-1197 - [i7]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 abs/2004.14257 (2020) - [i6]Amrith Setlur, Saket Dingliwal, Barnabás Póczos:
Covariate Distribution Aware Meta-learning. CoRR abs/2007.02523 (2020) - [i5]Amrith Setlur, Barnabás Póczos, Alan W. Black:
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. CoRR abs/2007.12948 (2020) - [i4]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 abs/2008.08148 (2020) - [i3]Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, Zachary C. Lipton:
Explaining The Efficacy of Counterfactually-Augmented Data. CoRR abs/2010.02114 (2020) - [i2]Amrith Setlur, Oscar Li, Virginia Smith:
Is Support Set Diversity Necessary for Meta-Learning? CoRR abs/2011.14048 (2020)
2010 – 2019
- 2019
- [i1]Amrith Setlur, Barnabás Póczos:
Better Approximate Inference for Partial Likelihood Models with a Latent Structure. CoRR abs/1910.10211 (2019)
Coauthor Index
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