Aditi Raghunathan

According to our database, Aditi Raghunathan authored at least 65 papers between 2015 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs.

Ziqian Zhong, Aditi Raghunathan

CoRR, August, 2025

Memorization Sinks: Isolating Memorization during LLM Training.

Gaurav R. Ghosal, Pratyush Maini, Aditi Raghunathan

CoRR, July, 2025

Reasoning as an Adaptive Defense for Safety.

Taeyoun Kim, Fahim Tajwar, Aditi Raghunathan, Aviral Kumar

CoRR, July, 2025

Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction.

Vaishnavh Nagarajan, Chen Henry Wu, Charles Ding, Aditi Raghunathan

CoRR, April, 2025

Weight Ensembling Improves Reasoning in Language Models.

Xingyu Dang, Christina Baek, Kaiyue Wen, Zico Kolter, Aditi Raghunathan

CoRR, April, 2025

Exact Unlearning of Finetuning Data via Model Merging at Scale.

Kevin Kuo, Amrith Setlur, Kartik Srinivas, Aditi Raghunathan, Virginia Smith

CoRR, April, 2025

Overtrained Language Models Are Harder to Fine-Tune.

Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen, Tanishq Kumar, Xiang Yue, Sadhika Malladi, Graham Neubig, Aditi Raghunathan

CoRR, March, 2025

Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions.

Emmy Liu, Amanda Bertsch, Lintang Sutawika, Lindia Tjuatja, Patrick Fernandes, Lara Marinov, Michael Chen, Shreya Singhal, Carolin Lawrence, Aditi Raghunathan, Kiril Gashteovski, Graham Neubig

CoRR, March, 2025

On the Feasibility of In-Context Probing for Data Attribution.

Cathy Jiao, Weizhen Gao, Aditi Raghunathan, Chenyan Xiong

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025

Dissecting Adversarial Robustness of Multimodal LM Agents.

Chen Henry Wu, Rishi Rajesh Shah, Jing Yu Koh, Russ Salakhutdinov, Daniel Fried, Aditi Raghunathan

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Repetition Improves Language Model Embeddings.

Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, Aditi Raghunathan

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Scaling Laws for Precision.

Tanishq Kumar, Zachary Ankner, Benjamin Frederick Spector, Blake Bordelon, Niklas Muennighoff, Mansheej Paul, Cengiz Pehlevan, Christopher Ré, Aditi Raghunathan

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance.

Sachin Goyal, Christina Baek, J. Zico Kolter, Aditi Raghunathan

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Theory of Agreement-on-the-Line in Linear Models and Gaussian Data.

Christina Baek, Aditi Raghunathan, J. Zico Kolter

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

Understanding the Influence of Synthetic Data for Text Embedders.

Jacob Mitchell Springer, Vaibhav Adlakha, Siva Reddy, Aditi Raghunathan, Marius Mosbach

Proceedings of the Findings of the Association for Computational Linguistics, 2025

Mitigating Bias in RAG: Controlling the Embedder.

Taeyoun Kim, Jacob Mitchell Springer, Aditi Raghunathan, Maarten Sap

Proceedings of the Findings of the Association for Computational Linguistics, 2025

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

Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions.

Jhih-Yi Hsieh, Aditi Raghunathan, Nihar B. Shah

CoRR, 2024

Adversarial Attacks on Multimodal Agents.

Chen Henry Wu, Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried, Aditi Raghunathan

CoRR, 2024

Jailbreaking is Best Solved by Definition.

Taeyoun Kim, Suhas Kotha, Aditi Raghunathan

CoRR, 2024

AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data.

Caroline Choi, Yoonho Lee, Annie S. Chen, Allan Zhou, Aditi Raghunathan, Chelsea Finn

CoRR, 2024

Predicting the Performance of Foundation Models via Agreement-on-the-Line.

Rahul Saxena, Taeyoun Kim, Aman Mehra, Christina Baek, J. Zico Kolter, Aditi Raghunathan

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line.

Eungyeup Kim, Mingjie Sun, Christina Baek, Aditi Raghunathan, J. Zico Kolter

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Understanding Finetuning for Factual Knowledge Extraction.

Gaurav Rohit Ghosal, Tatsunori Hashimoto, Aditi Raghunathan

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning.

Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

T-MARS: Improving Visual Representations by Circumventing Text Feature Learning.

Pratyush Maini, Sachin Goyal, Zachary Chase Lipton, J. Zico Kolter, Aditi Raghunathan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Understanding Catastrophic Forgetting in Language Models via Implicit Inference.

Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Why is SAM Robust to Label Noise?

Christina Baek, J. Zico Kolter, Aditi Raghunathan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Scaling Laws for Data Filtering - Data Curation Cannot be Compute Agnostic.

Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Reliable Test-Time Adaptation via Agreement-on-the-Line.

Eungyeup Kim, Mingjie Sun, Aditi Raghunathan, Zico Kolter

CoRR, 2023

ALP: Action-Aware Embodied Learning for Perception.

Xinran Liang, Anthony Han, Wilson Yan, Aditi Raghunathan, Pieter Abbeel

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Automatically Auditing Large Language Models via Discrete Optimization.

Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt

Proceedings of the International Conference on Machine Learning, 2023

Contextual Reliability: When Different Features Matter in Different Contexts.

Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan

Proceedings of the International Conference on Machine Learning, 2023

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Using Language to Extend to Unseen Domains.

Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang, Trevor Darrell, Joseph E. Gonzalez, Aditi Raghunathan, Anna Rohrbach

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Finetune like you pretrain: Improved finetuning of zero-shot vision models.

Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, Aditi Raghunathan

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift.

Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Test Time Adaptation via Conjugate Pseudo-labels.

Sachin Goyal, Mingjie Sun, Aditi Raghunathan, J. Zico Kolter

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift.

Christina Baek, Yiding Jiang, Aditi Raghunathan, J. Zico Kolter

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

An Explanation of In-context Learning as Implicit Bayesian Inference.

Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma

Proceedings of the Tenth International Conference on Learning Representations, 2022

Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, Percy Liang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Representations that Enable Generalization in Assistive Tasks.

Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca D. Dragan

Proceedings of the Conference on Robot Learning, 2022

2021

Adversarially robust machine learning with guarantees.

Aditi Raghunathan

PhD thesis, 2021

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization.

John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt

Proceedings of the 38th International Conference on Machine Learning, 2021

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices.

Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn

Proceedings of the 38th International Conference on Machine Learning, 2021

Just Train Twice: Improving Group Robustness without Training Group Information.

Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning.

Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn

CoRR, 2020

The Pitfalls of Simplicity Bias in Neural Networks.

Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming.

Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

An Investigation of Why Overparameterization Exacerbates Spurious Correlations.

Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang

Proceedings of the 37th International Conference on Machine Learning, 2020

Understanding and Mitigating the Tradeoff between Robustness and Accuracy.

Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang

Proceedings of the 37th International Conference on Machine Learning, 2020

DROCC: Deep Robust One-Class Classification.

Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain

Proceedings of the 37th International Conference on Machine Learning, 2020

Robust Encodings: A Framework for Combating Adversarial Typos.

Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Adversarial Training Can Hurt Generalization.

Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang

CoRR, 2019

Maximum Weighted Loss Discrepancy.

Fereshte Khani, Aditi Raghunathan, Percy Liang

CoRR, 2019

Unlabeled Data Improves Adversarial Robustness.

Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Certified Robustness to Adversarial Word Substitutions.

Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

2018

Semidefinite relaxations for certifying robustness to adversarial examples.

Aditi Raghunathan, Jacob Steinhardt, Percy Liang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Certified Defenses against Adversarial Examples.

Aditi Raghunathan, Jacob Steinhardt, Percy Liang

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Learning Mixture of Gaussians with Streaming Data.

Aditi Raghunathan, Prateek Jain, Ravishankar Krishnaswamy

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Estimating the unseen from multiple populations.

Aditi Raghunathan, Gregory Valiant, James Zou

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Estimation from Indirect Supervision with Linear Moments.

Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

A Reinforcement Learning Approach to Online Learning of Decision Trees.

Abhinav Garlapati, Aditi Raghunathan, Vaishnavh Nagarajan, Balaraman Ravindran

CoRR, 2015

Probabilistic Dependency Networks for Prediction and Diagnostics.

Narayanan Unny Edakunni, Aditi Raghunathan, Abhishek Tripathi, John Handley, Frédéric Roulland

CoRR, 2015