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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)- [c34]Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter:
Scaling Laws for Data Filtering - Data Curation Cannot be Compute Agnostic. CVPR 2024: 22702-22711 - [c33]Christina Baek, J. Zico Kolter, Aditi Raghunathan:
Why is SAM Robust to Label Noise? ICLR 2024 - [c32]Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan:
Understanding Catastrophic Forgetting in Language Models via Implicit Inference. ICLR 2024 - [c31]Pratyush Maini, Sachin Goyal, Zachary Chase Lipton, J. Zico Kolter, Aditi Raghunathan:
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. ICLR 2024 - [c30]Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan:
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. ICLR 2024 - [c29]Gaurav Rohit Ghosal, Tatsunori Hashimoto, Aditi Raghunathan:
Understanding Finetuning for Factual Knowledge Extraction. ICML 2024 - [i47]Caroline Choi, Yoonho Lee, Annie S. Chen, Allan Zhou, Aditi Raghunathan, Chelsea Finn:
AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data. CoRR abs/2401.10220 (2024) - [i46]Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, Aditi Raghunathan:
Repetition Improves Language Model Embeddings. CoRR abs/2402.15449 (2024) - [i45]Taeyoun Kim, Suhas Kotha, Aditi Raghunathan:
Jailbreaking is Best Solved by Definition. CoRR abs/2403.14725 (2024) - [i44]Aman Mehra, Rahul Saxena, Taeyoun Kim, Christina Baek, Zico Kolter, Aditi Raghunathan:
Predicting the Performance of Foundation Models via Agreement-on-the-Line. CoRR abs/2404.01542 (2024) - [i43]Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter:
Scaling Laws for Data Filtering - Data Curation cannot be Compute Agnostic. CoRR abs/2404.07177 (2024) - [i42]Christina Baek, Zico Kolter, Aditi Raghunathan:
Why is SAM Robust to Label Noise? CoRR abs/2405.03676 (2024) - [i41]Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan:
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. CoRR abs/2405.20439 (2024) - [i40]Chen Henry Wu, Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried, Aditi Raghunathan:
Adversarial Attacks on Multimodal Agents. CoRR abs/2406.12814 (2024) - [i39]Gaurav R. Ghosal, Tatsunori Hashimoto, Aditi Raghunathan:
Understanding Finetuning for Factual Knowledge Extraction. CoRR abs/2406.14785 (2024) - [i38]Sachin Goyal, Christina Baek, J. Zico Kolter, Aditi Raghunathan:
Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance. CoRR abs/2410.10796 (2024) - 2023
- [c28]Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, Aditi Raghunathan:
Finetune like you pretrain: Improved finetuning of zero-shot vision models. CVPR 2023: 19338-19347 - [c27]Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang, Trevor Darrell, Joseph E. Gonzalez, Aditi Raghunathan, Anna Rohrbach:
Using Language to Extend to Unseen Domains. ICLR 2023 - [c26]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 - [c25]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 - [c24]Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt:
Automatically Auditing Large Language Models via Discrete Optimization. ICML 2023: 15307-15329 - [c23]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 - [i37]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) - [i36]Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt:
Automatically Auditing Large Language Models via Discrete Optimization. CoRR abs/2303.04381 (2023) - [i35]Xinran Liang, Anthony Han, Wilson Yan, Aditi Raghunathan, Pieter Abbeel:
ALP: Action-Aware Embodied Learning for Perception. CoRR abs/2306.10190 (2023) - [i34]Pratyush Maini, Sachin Goyal, Zachary C. Lipton, J. Zico Kolter, Aditi Raghunathan:
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. CoRR abs/2307.03132 (2023) - [i33]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) - [i32]Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan:
Understanding Catastrophic Forgetting in Language Models via Implicit Inference. CoRR abs/2309.10105 (2023) - [i31]Eungyeup Kim, Mingjie Sun, Aditi Raghunathan, Zico Kolter:
Reliable Test-Time Adaptation via Agreement-on-the-Line. CoRR abs/2310.04941 (2023) - [i30]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) - [i29]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) - 2022
- [c22]Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca D. Dragan:
Learning Representations that Enable Generalization in Assistive Tasks. CoRL 2022: 2105-2114 - [c21]Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, Percy Liang:
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. ICLR 2022 - [c20]Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma:
An Explanation of In-context Learning as Implicit Bayesian Inference. ICLR 2022 - [c19]Christina Baek, Yiding Jiang, Aditi Raghunathan, J. Zico Kolter:
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift. NeurIPS 2022 - [c18]Sachin Goyal, Mingjie Sun, Aditi Raghunathan, J. Zico Kolter:
Test Time Adaptation via Conjugate Pseudo-labels. NeurIPS 2022 - [c17]Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan:
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. UAI 2022: 1041-1051 - [i28]Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, Percy Liang:
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. CoRR abs/2202.10054 (2022) - [i27]Christina Baek, Yiding Jiang, Aditi Raghunathan, Zico Kolter:
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift. CoRR abs/2206.13089 (2022) - [i26]Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan:
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. CoRR abs/2207.08977 (2022) - [i25]Sachin Goyal, Mingjie Sun, Aditi Raghunathan, Zico Kolter:
Test-Time Adaptation via Conjugate Pseudo-labels. CoRR abs/2207.09640 (2022) - [i24]Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang, Trevor Darrell, Joseph E. Gonzalez, Aditi Raghunathan, Anna Rohrbach:
Using Language to Extend to Unseen Domains. CoRR abs/2210.09520 (2022) - [i23]Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, Aditi Raghunathan:
Finetune like you pretrain: Improved finetuning of zero-shot vision models. CoRR abs/2212.00638 (2022) - [i22]Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown, Zackory Erickson, Anca D. Dragan:
Learning Representations that Enable Generalization in Assistive Tasks. CoRR abs/2212.03175 (2022) - 2021
- [b1]Aditi Raghunathan:
Adversarially robust machine learning with guarantees. Stanford University, USA, 2021 - [c16]Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn:
Just Train Twice: Improving Group Robustness without Training Group Information. ICML 2021: 6781-6792 - [c15]Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn:
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. ICML 2021: 6925-6935 - [c14]John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt:
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. ICML 2021: 7721-7735 - [i21]John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt:
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. CoRR abs/2107.04649 (2021) - [i20]Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn:
Just Train Twice: Improving Group Robustness without Training Group Information. CoRR abs/2107.09044 (2021) - [i19]Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma:
An Explanation of In-context Learning as Implicit Bayesian Inference. CoRR abs/2111.02080 (2021) - 2020
- [c13]Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang:
Robust Encodings: A Framework for Combating Adversarial Typos. ACL 2020: 2752-2765 - [c12]Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain:
DROCC: Deep Robust One-Class Classification. ICML 2020: 3711-3721 - [c11]Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang:
Understanding and Mitigating the Tradeoff between Robustness and Accuracy. ICML 2020: 7909-7919 - [c10]Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang:
An Investigation of Why Overparameterization Exacerbates Spurious Correlations. ICML 2020: 8346-8356 - [c9]Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli:
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. NeurIPS 2020 - [c8]Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS 2020 - [i18]Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang:
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. CoRR abs/2002.10716 (2020) - [i17]Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain:
DROCC: Deep Robust One-Class Classification. CoRR abs/2002.12718 (2020) - [i16]Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang:
Robust Encodings: A Framework for Combating Adversarial Typos. CoRR abs/2005.01229 (2020) - [i15]Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang:
An Investigation of Why Overparameterization Exacerbates Spurious Correlations. CoRR abs/2005.04345 (2020) - [i14]Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. CoRR abs/2006.07710 (2020) - [i13]Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn:
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning. CoRR abs/2008.02790 (2020) - [i12]Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli:
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. CoRR abs/2010.11645 (2020)
2010 – 2019
- 2019
- [c7]Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang:
Certified Robustness to Adversarial Word Substitutions. EMNLP/IJCNLP (1) 2019: 4127-4140 - [c6]Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang:
Unlabeled Data Improves Adversarial Robustness. NeurIPS 2019: 11190-11201 - [i11]Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi:
Unlabeled Data Improves Adversarial Robustness. CoRR abs/1905.13736 (2019) - [i10]Fereshte Khani, Aditi Raghunathan, Percy Liang:
Maximum Weighted Loss Discrepancy. CoRR abs/1906.03518 (2019) - [i9]Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang:
Adversarial Training Can Hurt Generalization. CoRR abs/1906.06032 (2019) - [i8]Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang:
Certified Robustness to Adversarial Word Substitutions. CoRR abs/1909.00986 (2019) - 2018
- [c5]Aditi Raghunathan, Jacob Steinhardt, Percy Liang:
Certified Defenses against Adversarial Examples. ICLR (Poster) 2018 - [c4]Aditi Raghunathan, Jacob Steinhardt, Percy Liang:
Semidefinite relaxations for certifying robustness to adversarial examples. NeurIPS 2018: 10900-10910 - [i7]Aditi Raghunathan, Jacob Steinhardt, Percy Liang:
Certified Defenses against Adversarial Examples. CoRR abs/1801.09344 (2018) - [i6]Aditi Raghunathan, Jacob Steinhardt, Percy Liang:
Semidefinite relaxations for certifying robustness to adversarial examples. CoRR abs/1811.01057 (2018) - 2017
- [c3]Aditi Raghunathan, Gregory Valiant, James Zou:
Estimating the unseen from multiple populations. ICML 2017: 2855-2863 - [c2]Aditi Raghunathan, Prateek Jain, Ravishankar Krishnaswamy:
Learning Mixture of Gaussians with Streaming Data. NIPS 2017: 6605-6614 - [i5]Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain:
Learning Mixture of Gaussians with Streaming Data. CoRR abs/1707.02391 (2017) - [i4]Aditi Raghunathan, Gregory Valiant, James Zou:
Estimating the unseen from multiple populations. CoRR abs/1707.03854 (2017) - 2016
- [c1]Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang:
Estimation from Indirect Supervision with Linear Moments. ICML 2016: 2568-2577 - [i3]Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang:
Estimation from Indirect Supervision with Linear Moments. CoRR abs/1608.03100 (2016) - 2015
- [i2]Abhinav Garlapati, Aditi Raghunathan, Vaishnavh Nagarajan, Balaraman Ravindran:
A Reinforcement Learning Approach to Online Learning of Decision Trees. CoRR abs/1507.06923 (2015) - [i1]Narayanan Unny Edakunni, Aditi Raghunathan, Abhishek Tripathi, John Handley, Frédéric Roulland:
Probabilistic Dependency Networks for Prediction and Diagnostics. CoRR abs/1508.03130 (2015)
Coauthor Index
J. Zico Kolter
aka: Zico Kolter
aka: Zico Kolter
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last updated on 2024-11-25 22:43 CET by the dblp team
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