Aditi Raghunathan

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 77

Top Topics

Visual Representations

Top Venues

Publications

Christina Baek, J. Zico Kolter, Aditi Raghunathan
Why is SAM Robust to Label Noise? ICLR (2024)
Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. ICLR (2024)
Aman Mehra, Rahul Saxena, Taeyoun Kim, Christina Baek, Zico Kolter, Aditi Raghunathan
Predicting the Performance of Foundation Models via Agreement-on-the-Line. CoRR (2024)
Christina Baek, Zico Kolter, Aditi Raghunathan
Why is SAM Robust to Label Noise? CoRR (2024)
Chen Henry Wu, Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried, Aditi Raghunathan
Adversarial Attacks on Multimodal Agents. CoRR (2024)
Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter
Scaling Laws for Data Filtering - Data Curation cannot be Compute Agnostic. CoRR (2024)
Gaurav R. Ghosal, Tatsunori Hashimoto, Aditi Raghunathan
Understanding Finetuning for Factual Knowledge Extraction. CoRR (2024)
Pratyush Maini, Sachin Goyal, Zachary Chase Lipton, J. Zico Kolter, Aditi Raghunathan
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. ICLR (2024)
Taeyoun Kim, Suhas Kotha, Aditi Raghunathan
Jailbreaking is Best Solved by Definition. CoRR (2024)
Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, Aditi Raghunathan
Repetition Improves Language Model Embeddings. CoRR (2024)
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)
Caroline Choi, Yoonho Lee, Annie S. Chen, Allan Zhou, Aditi Raghunathan, Chelsea Finn
AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data. CoRR (2024)
Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan
Understanding Catastrophic Forgetting in Language Models via Implicit Inference. ICLR (2024)
Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. CoRR (2024)
Xinran Liang, Anthony Han, Wilson Yan, Aditi Raghunathan, Pieter Abbeel
ALP: Action-Aware Embodied Learning for Perception. CoRR (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. ICLR (2023)
Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
Multitask Learning Can Improve Worst-Group Outcomes. CoRR (2023)
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)
Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, Aditi Raghunathan
Finetune like you pretrain: Improved finetuning of zero-shot vision models. CVPR (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)
Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt
Automatically Auditing Large Language Models via Discrete Optimization. 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)
Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan
Understanding Catastrophic Forgetting in Language Models via Implicit Inference. 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)
Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt
Automatically Auditing Large Language Models via Discrete Optimization. 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)
Eungyeup Kim, Mingjie Sun, Aditi Raghunathan, Zico Kolter
Reliable Test-Time Adaptation via Agreement-on-the-Line. CoRR (2023)
Pratyush Maini, Sachin Goyal, Zachary C. Lipton, J. Zico Kolter, Aditi Raghunathan
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. CoRR (2023)
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, Percy Liang
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. CoRR (2022)
Sachin Goyal, Mingjie Sun, Aditi Raghunathan, J. Zico Kolter
Test Time Adaptation via Conjugate Pseudo-labels. NeurIPS (2022)
Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. UAI (2022)
Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. CoRR (2022)
Sachin Goyal, Mingjie Sun, Aditi Raghunathan, Zico Kolter
Test-Time Adaptation via Conjugate Pseudo-labels. CoRR (2022)
Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown, Zackory Erickson, Anca D. Dragan
Learning Representations that Enable Generalization in Assistive Tasks. CoRR (2022)
Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma
An Explanation of In-context Learning as Implicit Bayesian Inference. ICLR (2022)
Christina Baek, Yiding Jiang, Aditi Raghunathan, Zico Kolter
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift. CoRR (2022)
Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, Aditi Raghunathan
Finetune like you pretrain: Improved finetuning of zero-shot vision models. CoRR (2022)
Christina Baek, Yiding Jiang, Aditi Raghunathan, J. Zico Kolter
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift. NeurIPS (2022)
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 (2022)
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, Percy Liang
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. ICLR (2022)
Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca D. Dragan
Learning Representations that Enable Generalization in Assistive Tasks. CoRL (2022)
Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. ICML (2021)
Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma
An Explanation of In-context Learning as Implicit Bayesian Inference. CoRR (2021)
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 (2021)
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 (2021)
John P. 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)
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)
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS (2020)
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang
An Investigation of Why Overparameterization Exacerbates Spurious Correlations. CoRR (2020)
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 (2020)
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
DROCC: Deep Robust One-Class Classification. ICML (2020)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Understanding and Mitigating the Tradeoff between Robustness and Accuracy. ICML (2020)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. CoRR (2020)
Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning. CoRR (2020)
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)
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang
An Investigation of Why Overparameterization Exacerbates Spurious Correlations. ICML (2020)
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
The Pitfalls of Simplicity Bias in Neural Networks. CoRR (2020)
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
DROCC: Deep Robust One-Class Classification. CoRR (2020)
Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang
Robust Encodings: A Framework for Combating Adversarial Typos. ACL (2020)
Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang
Robust Encodings: A Framework for Combating Adversarial Typos. CoRR (2020)
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi
Unlabeled Data Improves Adversarial Robustness. CoRR (2019)
Fereshte Khani, Aditi Raghunathan, Percy Liang
Maximum Weighted Loss Discrepancy. CoRR (2019)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Adversarial Training Can Hurt Generalization. CoRR (2019)
Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang
Certified Robustness to Adversarial Word Substitutions. CoRR (2019)
Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang
Certified Robustness to Adversarial Word Substitutions. EMNLP/IJCNLP (1) (2019)
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang
Unlabeled Data Improves Adversarial Robustness. NeurIPS (2019)
Aditi Raghunathan, Jacob Steinhardt, Percy Liang
Certified Defenses against Adversarial Examples. CoRR (2018)
Aditi Raghunathan, Jacob Steinhardt, Percy Liang
Semidefinite relaxations for certifying robustness to adversarial examples. NeurIPS (2018)
Aditi Raghunathan, Jacob Steinhardt, Percy Liang
Certified Defenses against Adversarial Examples. ICLR (Poster) (2018)
Aditi Raghunathan, Jacob Steinhardt, Percy Liang
Semidefinite relaxations for certifying robustness to adversarial examples. CoRR (2018)
Aditi Raghunathan, Gregory Valiant, James Zou
Estimating the unseen from multiple populations. ICML (2017)
Aditi Raghunathan, Gregory Valiant, James Zou
Estimating the unseen from multiple populations. CoRR (2017)
Aditi Raghunathan, Prateek Jain, Ravishankar Krishnaswamy
Learning Mixture of Gaussians with Streaming Data. NIPS (2017)
Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain
Learning Mixture of Gaussians with Streaming Data. CoRR (2017)
Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
Estimation from Indirect Supervision with Linear Moments. CoRR (2016)
Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
Estimation from Indirect Supervision with Linear Moments. ICML (2016)
Abhinav Garlapati, Aditi Raghunathan, Vaishnavh Nagarajan, Balaraman Ravindran
A Reinforcement Learning Approach to Online Learning of Decision Trees. CoRR (2015)
Narayanan Unny Edakunni, Aditi Raghunathan, Abhishek Tripathi, John Handley, Frédéric Roulland
Probabilistic Dependency Networks for Prediction and Diagnostics. CoRR (2015)