Probabilistic Dependency Networks for Prediction and Diagnostics. - Narayanan Unny Edakunni; Aditi Raghunathan; Abhishek Tripathi; John Handley; Frédéric Roulland (CoRR, 2015)

A Reinforcement Learning Approach to Online Learning of Decision Trees. - Abhinav Garlapati; Aditi Raghunathan; Vaishnavh Nagarajan; Balaraman Ravindran (CoRR, 2015)

Estimation from Indirect Supervision with Linear Moments. - Aditi Raghunathan; Roy Frostig; John C. Duchi; Percy Liang (ICML, 2016)

Estimating the unseen from multiple populations. - Aditi Raghunathan; Gregory Valiant; James Zou 001 (ICML, 2017)

Learning Mixture of Gaussians with Streaming Data. - Aditi Raghunathan; Prateek Jain 002; Ravishankar Krishnaswamy (NIPS, 2017)

Certified Defenses against Adversarial Examples. - Aditi Raghunathan; Jacob Steinhardt; Percy Liang (ICLR (Poster), 2018)

Semidefinite relaxations for certifying robustness to adversarial examples. - Aditi Raghunathan; Jacob Steinhardt; Percy Liang (NeurIPS, 2018)

Certified Robustness to Adversarial Word Substitutions. - Robin Jia; Aditi Raghunathan; Kerem Göksel; Percy Liang (EMNLP/IJCNLP (1), 2019)

Unlabeled Data Improves Adversarial Robustness. - Yair Carmon; Aditi Raghunathan; Ludwig Schmidt; John C. Duchi; Percy Liang (NeurIPS, 2019)

Maximum Weighted Loss Discrepancy. - Fereshte Khani; Aditi Raghunathan; Percy Liang (CoRR, 2019)

Adversarial Training Can Hurt Generalization. - Aditi Raghunathan; Sang Michael Xie; Fanny Yang; John C. Duchi; Percy Liang (CoRR, 2019)

Robust Encodings: A Framework for Combating Adversarial Typos. - Erik Jones; Robin Jia; Aditi Raghunathan; Percy Liang (ACL, 2020)

DROCC: Deep Robust One-Class Classification. - Sachin Goyal; Aditi Raghunathan; Moksh Jain; Harsha Vardhan Simhadri; Prateek Jain 002 (ICML, 2020)

Understanding and Mitigating the Tradeoff between Robustness and Accuracy. - Aditi Raghunathan; Sang Michael Xie; Fanny Yang; John C. Duchi; Percy Liang (ICML, 2020)

An Investigation of Why Overparameterization Exacerbates Spurious Correlations. - Shiori Sagawa; Aditi Raghunathan; Pang Wei Koh; Percy Liang (ICML, 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 (NeurIPS, 2020)

The Pitfalls of Simplicity Bias in Neural Networks. - Harshay Shah; Kaustav Tamuly; Aditi Raghunathan; Prateek Jain 002; Praneeth Netrapalli (NeurIPS, 2020)

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning. - Evan Zheran Liu; Aditi Raghunathan; Percy Liang; Chelsea Finn (CoRR, 2020)

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 (ICML, 2021)

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. - Evan Zheran Liu; Aditi Raghunathan; Percy Liang; Chelsea Finn (ICML, 2021)

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. - John Miller 001; Rohan Taori; Aditi Raghunathan; Shiori Sagawa; Pang Wei Koh; Vaishaal Shankar; Percy Liang; Yair Carmon; Ludwig Schmidt (ICML, 2021)

Adversarially robust machine learning with guarantees. - Aditi Raghunathan (2021)

Learning Representations that Enable Generalization in Assistive Tasks. - Jerry Zhi-Yang He; Zackory Erickson; Daniel S. Brown; Aditi Raghunathan; Anca D. Dragan (CoRL, 2022)

Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. - Ananya Kumar; Aditi Raghunathan; Robbie Matthew Jones; Tengyu Ma 001; Percy Liang (ICLR, 2022)

An Explanation of In-context Learning as Implicit Bayesian Inference. - Sang Michael Xie; Aditi Raghunathan; Percy Liang; Tengyu Ma 001 (ICLR, 2022)

Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift. - Christina Baek; Yiding Jiang; Aditi Raghunathan; J. Zico Kolter (NeurIPS, 2022)

Test Time Adaptation via Conjugate Pseudo-labels. - Sachin Goyal; Mingjie Sun; Aditi Raghunathan; J. Zico Kolter (NeurIPS, 2022)

Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. - Ananya Kumar; Tengyu Ma 001; Percy Liang; Aditi Raghunathan (UAI, 2022)

Finetune like you pretrain: Improved finetuning of zero-shot vision models. - Sachin Goyal; Ananya Kumar; Sankalp Garg; Zico Kolter; Aditi Raghunathan (CVPR, 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 (ICLR, 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 (ICLR, 2023)

Contextual Reliability: When Different Features Matter in Different Contexts. - Gaurav Rohit Ghosal; Amrith Setlur; Daniel S. Brown; Anca D. Dragan; Aditi Raghunathan (ICML, 2023)

Automatically Auditing Large Language Models via Discrete Optimization. - Erik Jones; Anca D. Dragan; Aditi Raghunathan; Jacob Steinhardt (ICML, 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 (NeurIPS, 2023)

ALP: Action-Aware Embodied Learning for Perception. - Xinran Liang; Anthony Han; Wilson Yan; Aditi Raghunathan; Pieter Abbeel (CoRR, 2023)

Reliable Test-Time Adaptation via Agreement-on-the-Line. - Eungyeup Kim; Mingjie Sun; Aditi Raghunathan; Zico Kolter (CoRR, 2023)

Scaling Laws for Data Filtering - Data Curation Cannot be Compute Agnostic. - Sachin Goyal; Pratyush Maini; Zachary C. Lipton; Aditi Raghunathan; J. Zico Kolter (CVPR, 2024)

Why is SAM Robust to Label Noise? - Christina Baek; J. Zico Kolter; Aditi Raghunathan (ICLR, 2024)

Understanding Catastrophic Forgetting in Language Models via Implicit Inference. - Suhas Kotha; Jacob Mitchell Springer; Aditi Raghunathan (ICLR, 2024)

T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. - Pratyush Maini; Sachin Goyal; Zachary Chase Lipton; J. Zico Kolter; Aditi Raghunathan (ICLR, 2024)

Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. - Jacob Mitchell Springer; Vaishnavh Nagarajan; Aditi Raghunathan (ICLR, 2024)

Understanding Finetuning for Factual Knowledge Extraction. - Gaurav Rohit Ghosal; Tatsunori Hashimoto; Aditi Raghunathan (ICML, 2024)

Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line. - Eungyeup Kim; Mingjie Sun; Christina Baek; Aditi Raghunathan; J. Zico Kolter (NeurIPS, 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 (NeurIPS, 2024)

AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data. - Caroline Choi; Yoonho Lee 001; Annie S. Chen; Allan Zhou; Aditi Raghunathan; Chelsea Finn (CoRR, 2024)

Jailbreaking is Best Solved by Definition. - Taeyoun Kim; Suhas Kotha; Aditi Raghunathan (CoRR, 2024)

Adversarial Attacks on Multimodal Agents. - Chen Henry Wu; Jing Yu Koh; Ruslan Salakhutdinov; Daniel Fried; Aditi Raghunathan (CoRR, 2024)

Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions. - Jhih-Yi Hsieh; Aditi Raghunathan; Nihar B. Shah (CoRR, 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)

Mitigating Bias in RAG: Controlling the Embedder. - Taeyoun Kim; Jacob Mitchell Springer; Aditi Raghunathan; Maarten Sap (ACL (Findings), 2025)

Understanding the Influence of Synthetic Data for Text Embedders. - Jacob Mitchell Springer; Vaibhav Adlakha; Siva Reddy; Aditi Raghunathan; Marius Mosbach (ACL (Findings), 2025)

Theory of Agreement-on-the-Line in Linear Models and Gaussian Data. - Christina Baek; Aditi Raghunathan; J. Zico Kolter (AISTATS, 2025)

Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance. - Sachin Goyal; Christina Baek; J. Zico Kolter; Aditi Raghunathan (ICLR, 2025)

Scaling Laws for Precision. - Tanishq Kumar; Zachary Ankner; Benjamin Frederick Spector; Blake Bordelon; Niklas Muennighoff; Mansheej Paul; Cengiz Pehlevan; Christopher Ré; Aditi Raghunathan (ICLR, 2025)

Repetition Improves Language Model Embeddings. - Jacob Mitchell Springer; Suhas Kotha; Daniel Fried; Graham Neubig; Aditi Raghunathan (ICLR, 2025)

Dissecting Adversarial Robustness of Multimodal LM Agents. - Chen Henry Wu; Rishi Rajesh Shah; Jing Yu Koh; Russ Salakhutdinov; Daniel Fried; Aditi Raghunathan (ICLR, 2025)

On the Feasibility of In-Context Probing for Data Attribution. - Cathy Jiao; Weizhen Gao; Aditi Raghunathan; Chenyan Xiong (NAACL (Findings), 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, 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, 2025)

Exact Unlearning of Finetuning Data via Model Merging at Scale. - Kevin Kuo; Amrith Setlur; Kartik Srinivas; Aditi Raghunathan; Virginia Smith (CoRR, 2025)

Weight Ensembling Improves Reasoning in Language Models. - Xingyu Dang; Christina Baek; Kaiyue Wen; Zico Kolter; Aditi Raghunathan (CoRR, 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, 2025)

Reasoning as an Adaptive Defense for Safety. - Taeyoun Kim; Fahim Tajwar; Aditi Raghunathan; Aviral Kumar (CoRR, 2025)

Memorization Sinks: Isolating Memorization during LLM Training. - Gaurav R. Ghosal; Pratyush Maini; Aditi Raghunathan (CoRR, 2025)

Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs. - Ziqian Zhong; Aditi Raghunathan (CoRR, 2025)