Aditi Raghunathan

118 papers A* 40A 2Misc 1Journal 68Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	One-step Language Modeling via Continuous Denoising. CoRR Chanhyuk Lee, Jaehoon Yoo, Manan Agarwal, Sheel Shah, Jerry Huang, Aditi Raghunathan, Seunghoon Hong, Nicholas M. Boffi, Jinwoo Kim
2026	J	`jnl`	S2D: Selective Spectral Decay for Quantization-Friendly Conditioning of Neural Activations. CoRR Arnav Chavan, Nahush Lele, Udbhav Bamba, Sankalp Dayal, Aditi Raghunathan, Deepak K. Gupta
2026	J	`jnl`	The Finetuner's Fallacy: When to Pretrain with Your Finetuning Data. CoRR Christina Baek, Ricardo Pio Monti, David Schwab, Amro Abbas, Rishabh Adiga, Cody Blakeney, Maximilian Böther, Paul Burstein, Aldo Gael Carranza, Alvin Deng, Parth Doshi, Vineeth Dorna, Alex Fang, Tony Jiang, Siddharth Joshi, Brett W. Larsen, Jason Chan Lee, Katherine L. Mentzer, Luke Merrick, Haakon Mongstad, Fan Pan, Anshuman Suri, Darren Teh, Jason Telanoff, Jack Urbanek, Zhengping Wang, Josh Wills, Haoli Yin, Aditi Raghunathan, J. Zico Kolter, Bogdan Gaza, Ari Morcos, Matthew L. Leavitt, Pratyush Maini
2025	A*	`conf`	Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance. ICLR Sachin Goyal, Christina Baek, J. Zico Kolter, Aditi Raghunathan
2025	J	`jnl`	Differential Smoothing Mitigates Sharpening and Improves LLM Reasoning. CoRR Jingchu Gai, Guanning Zeng, Huaqing Zhang, Aditi Raghunathan
2025	A*	`conf`	Dissecting Adversarial Robustness of Multimodal LM Agents. ICLR Chen Henry Wu, Rishi Rajesh Shah, Jing Yu Koh, Russ Salakhutdinov, Daniel Fried, Aditi Raghunathan
2025	J	`jnl`	Exact Unlearning of Finetuning Data via Model Merging at Scale. CoRR Kevin Kuo, Amrith Setlur, Kartik Srinivas, Aditi Raghunathan, Virginia Smith
2025	J	`jnl`	ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases. CoRR Ziqian Zhong, Aditi Raghunathan, Nicholas Carlini
2025	J	`jnl`	Jailbreaking in the Haystack. CoRR Rishi Rajesh Shah, Chen Henry Wu, Shashwat Saxena, Ziqian Zhong, Alexander Robey, Aditi Raghunathan
2025	A*	`conf`	Memorization Sinks: Isolating Memorization during LLM Training. ICML Gaurav Rohit Ghosal, Pratyush Maini, Aditi Raghunathan
2025	J	`jnl`	Memorization Sinks: Isolating Memorization during LLM Training. CoRR Gaurav R. Ghosal, Pratyush Maini, Aditi Raghunathan
2025	—	`conf`	Mitigating Bias in RAG: Controlling the Embedder. ACL (Findings) Taeyoun Kim, Jacob Mitchell Springer, Aditi Raghunathan, Maarten Sap
2025	J	`jnl`	Mitigating Bias in RAG: Controlling the Embedder. CoRR Taeyoun Kim, Jacob Mitchell Springer, Aditi Raghunathan, Maarten Sap
2025	J	`jnl`	Mitigating Modal Imbalance in Multimodal Reasoning. CoRR Chen Henry Wu, Neil Kale, Aditi Raghunathan
2025	J	`jnl`	Mode-Conditioning Unlocks Superior Test-Time Scaling. CoRR Chen Henry Wu, Sachin Goyal, Aditi Raghunathan
2025	A*	`conf`	Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions. EMNLP Emmy Liu, Amanda Bertsch, Lintang Sutawika, Lindia Tjuatja, Patrick Fernandes, Lara Marinov, Michael Chen, Shreya Singhal, Carolin Lawrence, Aditi Raghunathan, Kiril Gashteovski, Graham Neubig
2025	J	`jnl`	Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions. CoRR Emmy Liu, Amanda Bertsch, Lintang Sutawika, Lindia Tjuatja, Patrick Fernandes, Lara Marinov, Michael Chen, Shreya Singhal, Carolin Lawrence, Aditi Raghunathan, Kiril Gashteovski, Graham Neubig
2025	—	`conf`	On the Feasibility of In-Context Probing for Data Attribution. NAACL (Findings) Cathy Jiao, Weizhen Gao, Aditi Raghunathan, Chenyan Xiong
2025	A*	`conf`	Overtrained Language Models Are Harder to Fine-Tune. ICML Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen, Tanishq Kumar, Xiang Yue, Sadhika Malladi, Graham Neubig, Aditi Raghunathan
2025	J	`jnl`	Overtrained Language Models Are Harder to Fine-Tune. CoRR Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen, Tanishq Kumar, Xiang Yue, Sadhika Malladi, Graham Neubig, Aditi Raghunathan
2025	J	`jnl`	Reasoning as an Adaptive Defense for Safety. CoRR Taeyoun Kim, Fahim Tajwar, Aditi Raghunathan, Aviral Kumar
2025	A*	`conf`	Repetition Improves Language Model Embeddings. ICLR Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, Aditi Raghunathan
2025	A*	`conf`	Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction. ICML Vaishnavh Nagarajan, Chen Henry Wu, Charles Ding, Aditi Raghunathan
2025	J	`jnl`	Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction. CoRR Vaishnavh Nagarajan, Chen Henry Wu, Charles Ding, Aditi Raghunathan
2025	A*	`conf`	Scaling Laws for Precision. ICLR Tanishq Kumar, Zachary Ankner, Benjamin Frederick Spector, Blake Bordelon, Niklas Muennighoff, Mansheej Paul, Cengiz Pehlevan, Christopher Ré, Aditi Raghunathan
2025	A	`conf`	Theory of Agreement-on-the-Line in Linear Models and Gaussian Data. AISTATS Christina Baek, Aditi Raghunathan, J. Zico Kolter
2025	—	`conf`	Understanding the Influence of Synthetic Data for Text Embedders. ACL (Findings) Jacob Mitchell Springer, Vaibhav Adlakha, Siva Reddy, Aditi Raghunathan, Marius Mosbach
2025	J	`jnl`	Understanding the Influence of Synthetic Data for Text Embedders. CoRR Jacob Mitchell Springer, Vaibhav Adlakha, Siva Reddy, Aditi Raghunathan, Marius Mosbach
2025	A*	`conf`	Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions. USENIX Security Symposium Jhih-Yi Hsieh, Aditi Raghunathan, Nihar B. Shah
2025	J	`jnl`	Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs. CoRR Ziqian Zhong, Aditi Raghunathan
2025	J	`jnl`	Weight Ensembling Improves Reasoning in Language Models. CoRR Xingyu Dang, Christina Baek, Kaiyue Wen, Zico Kolter, Aditi Raghunathan
2024	J	`jnl`	Adversarial Attacks on Multimodal Agents. CoRR Chen Henry Wu, Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried, Aditi Raghunathan
2024	J	`jnl`	AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data. CoRR Caroline Choi, Yoonho Lee, Annie S. Chen, Allan Zhou, Aditi Raghunathan, Chelsea Finn
2024	J	`jnl`	Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance. CoRR Sachin Goyal, Christina Baek, J. Zico Kolter, Aditi Raghunathan
2024	J	`jnl`	Jailbreaking is Best Solved by Definition. CoRR Taeyoun Kim, Suhas Kotha, Aditi Raghunathan
2024	J	`jnl`	Multitask Learning Can Improve Worst-Group Outcomes. Trans. Mach. Learn. Res. Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
2024	A*	`conf`	Predicting the Performance of Foundation Models via Agreement-on-the-Line. NeurIPS Rahul Saxena, Taeyoun Kim, Aman Mehra, Christina Baek, J. Zico Kolter, Aditi Raghunathan
2024	J	`jnl`	Predicting the Performance of Foundation Models via Agreement-on-the-Line. CoRR Aman Mehra, Rahul Saxena, Taeyoun Kim, Christina Baek, Zico Kolter, Aditi Raghunathan
2024	J	`jnl`	Repetition Improves Language Model Embeddings. CoRR Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, Aditi Raghunathan
2024	A*	`conf`	Scaling Laws for Data Filtering - Data Curation Cannot be Compute Agnostic. CVPR Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter
2024	J	`jnl`	Scaling Laws for Data Filtering - Data Curation cannot be Compute Agnostic. CoRR Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter
2024	J	`jnl`	Scaling Laws for Precision. CoRR Tanishq Kumar, Zachary Ankner, Benjamin Spector, Blake Bordelon, Niklas Muennighoff, Mansheej Paul, Cengiz Pehlevan, Christopher Ré, Aditi Raghunathan
2024	A*	`conf`	Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. ICLR Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan
2024	J	`jnl`	Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. CoRR Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan
2024	A*	`conf`	T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. ICLR Pratyush Maini, Sachin Goyal, Zachary Chase Lipton, J. Zico Kolter, Aditi Raghunathan
2024	A*	`conf`	Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line. NeurIPS Eungyeup Kim, Mingjie Sun, Christina Baek, Aditi Raghunathan, J. Zico Kolter
2024	A*	`conf`	Understanding Catastrophic Forgetting in Language Models via Implicit Inference. ICLR Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan
2024	A*	`conf`	Understanding Finetuning for Factual Knowledge Extraction. ICML Gaurav Rohit Ghosal, Tatsunori Hashimoto, Aditi Raghunathan
2024	J	`jnl`	Understanding Finetuning for Factual Knowledge Extraction. CoRR Gaurav R. Ghosal, Tatsunori Hashimoto, Aditi Raghunathan
2024	J	`jnl`	Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions. CoRR Jhih-Yi Hsieh, Aditi Raghunathan, Nihar B. Shah
2024	A*	`conf`	Why is SAM Robust to Label Noise? ICLR Christina Baek, J. Zico Kolter, Aditi Raghunathan
2024	J	`jnl`	Why is SAM Robust to Label Noise? CoRR Christina Baek, Zico Kolter, Aditi Raghunathan
2023	J	`jnl`	ALP: Action-Aware Embodied Learning for Perception. CoRR Xinran Liang, Anthony Han, Wilson Yan, Aditi Raghunathan, Pieter Abbeel
2023	A*	`conf`	Automatically Auditing Large Language Models via Discrete Optimization. ICML Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt
2023	J	`jnl`	Automatically Auditing Large Language Models via Discrete Optimization. CoRR Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt
2023	A*	`conf`	Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. ICLR Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
2023	J	`jnl`	Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. CoRR Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
2023	A*	`conf`	Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
2023	J	`jnl`	Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
2023	A*	`conf`	Contextual Reliability: When Different Features Matter in Different Contexts. ICML Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
2023	J	`jnl`	Contextual Reliability: When Different Features Matter in Different Contexts. CoRR Gaurav R. Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
2023	A*	`conf`	Finetune like you pretrain: Improved finetuning of zero-shot vision models. CVPR Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, Aditi Raghunathan
2023	J	`jnl`	Multitask Learning Can Improve Worst-Group Outcomes. CoRR Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
2023	J	`jnl`	Reliable Test-Time Adaptation via Agreement-on-the-Line. CoRR Eungyeup Kim, Mingjie Sun, Aditi Raghunathan, Zico Kolter
2023	J	`jnl`	T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. CoRR Pratyush Maini, Sachin Goyal, Zachary C. Lipton, J. Zico Kolter, Aditi Raghunathan
2023	J	`jnl`	Understanding Catastrophic Forgetting in Language Models via Implicit Inference. CoRR Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan
2023	A*	`conf`	Using Language to Extend to Unseen Domains. ICLR Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang, Trevor Darrell, Joseph E. Gonzalez, Aditi Raghunathan, Anna Rohrbach
2022	J	`jnl`	Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift. CoRR Christina Baek, Yiding Jiang, Aditi Raghunathan, Zico Kolter
2022	A*	`conf`	Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift. NeurIPS Christina Baek, Yiding Jiang, Aditi Raghunathan, J. Zico Kolter
2022	A*	`conf`	An Explanation of In-context Learning as Implicit Bayesian Inference. ICLR Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma
2022	A	`conf`	Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. UAI Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan
2022	J	`jnl`	Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. CoRR Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan
2022	A*	`conf`	Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. ICLR Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, Percy Liang
2022	J	`jnl`	Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. CoRR Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, Percy Liang
2022	J	`jnl`	Finetune like you pretrain: Improved finetuning of zero-shot vision models. CoRR Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, Aditi Raghunathan
2022	Misc	`conf`	Learning Representations that Enable Generalization in Assistive Tasks. CoRL Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca D. Dragan
2022	J	`jnl`	Learning Representations that Enable Generalization in Assistive Tasks. CoRR Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown, Zackory Erickson, Anca D. Dragan
2022	A*	`conf`	Test Time Adaptation via Conjugate Pseudo-labels. NeurIPS Sachin Goyal, Mingjie Sun, Aditi Raghunathan, J. Zico Kolter
2022	J	`jnl`	Test-Time Adaptation via Conjugate Pseudo-labels. CoRR Sachin Goyal, Mingjie Sun, Aditi Raghunathan, Zico Kolter
2022	J	`jnl`	Using Language to Extend to Unseen Domains. CoRR Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang, Trevor Darrell, Joseph E. Gonzalez, Aditi Raghunathan, Anna Rohrbach
2021	J	`jnl`	Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. CoRR John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt
2021	A*	`conf`	Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. ICML John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt
2021	—	`—`	Adversarially robust machine learning with guarantees. Aditi Raghunathan
2021	J	`jnl`	An Explanation of In-context Learning as Implicit Bayesian Inference. CoRR Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma
2021	A*	`conf`	Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. ICML Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
2021	A*	`conf`	Just Train Twice: Improving Group Robustness without Training Group Information. ICML Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn
2021	J	`jnl`	Just Train Twice: Improving Group Robustness without Training Group Information. CoRR Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn
2020	A*	`conf`	An Investigation of Why Overparameterization Exacerbates Spurious Correlations. ICML Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang
2020	J	`jnl`	An Investigation of Why Overparameterization Exacerbates Spurious Correlations. CoRR Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang
2020	A*	`conf`	DROCC: Deep Robust One-Class Classification. ICML Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
2020	J	`jnl`	DROCC: Deep Robust One-Class Classification. CoRR Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
2020	A*	`conf`	Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. NeurIPS Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli
2020	J	`jnl`	Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. CoRR Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli
2020	J	`jnl`	Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning. CoRR Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
2020	A*	`conf`	Robust Encodings: A Framework for Combating Adversarial Typos. ACL Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang
2020	J	`jnl`	Robust Encodings: A Framework for Combating Adversarial Typos. CoRR Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang
2020	A*	`conf`	The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
2020	J	`jnl`	The Pitfalls of Simplicity Bias in Neural Networks. CoRR Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
2020	J	`jnl`	Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. CoRR Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
2020	A*	`conf`	Understanding and Mitigating the Tradeoff between Robustness and Accuracy. ICML Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
2019	J	`jnl`	Adversarial Training Can Hurt Generalization. CoRR Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
2019	—	`conf`	Certified Robustness to Adversarial Word Substitutions. EMNLP/IJCNLP (1) Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang
2019	J	`jnl`	Certified Robustness to Adversarial Word Substitutions. CoRR Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang
2019	J	`jnl`	Maximum Weighted Loss Discrepancy. CoRR Fereshte Khani, Aditi Raghunathan, Percy Liang
2019	A*	`conf`	Unlabeled Data Improves Adversarial Robustness. NeurIPS Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang
2019	J	`jnl`	Unlabeled Data Improves Adversarial Robustness. CoRR Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi
2018	—	`conf`	Certified Defenses against Adversarial Examples. ICLR (Poster) Aditi Raghunathan, Jacob Steinhardt, Percy Liang
2018	J	`jnl`	Certified Defenses against Adversarial Examples. CoRR Aditi Raghunathan, Jacob Steinhardt, Percy Liang
2018	A*	`conf`	Semidefinite relaxations for certifying robustness to adversarial examples. NeurIPS Aditi Raghunathan, Jacob Steinhardt, Percy Liang
2018	J	`jnl`	Semidefinite relaxations for certifying robustness to adversarial examples. CoRR Aditi Raghunathan, Jacob Steinhardt, Percy Liang
2017	A*	`conf`	Estimating the unseen from multiple populations. ICML Aditi Raghunathan, Gregory Valiant, James Zou
2017	J	`jnl`	Estimating the unseen from multiple populations. CoRR Aditi Raghunathan, Gregory Valiant, James Zou
2017	—	`conf`	Learning Mixture of Gaussians with Streaming Data. NIPS Aditi Raghunathan, Prateek Jain, Ravishankar Krishnaswamy
2017	J	`jnl`	Learning Mixture of Gaussians with Streaming Data. CoRR Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain
2016	A*	`conf`	Estimation from Indirect Supervision with Linear Moments. ICML Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
2016	J	`jnl`	Estimation from Indirect Supervision with Linear Moments. CoRR Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
2015	J	`jnl`	A Reinforcement Learning Approach to Online Learning of Decision Trees. CoRR Abhinav Garlapati, Aditi Raghunathan, Vaishnavh Nagarajan, Balaraman Ravindran
2015	J	`jnl`	Probabilistic Dependency Networks for Prediction and Diagnostics. CoRR Narayanan Unny Edakunni, Aditi Raghunathan, Abhishek Tripathi, John Handley, Frédéric Roulland