![](https://dblp.uni-trier.de/img/logo.ua.320x120.png)
![](https://dblp.uni-trier.de/img/dropdown.dark.16x16.png)
![](https://dblp.uni-trier.de/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp.uni-trier.de/img/search.dark.16x16.png)
![search dblp](https://dblp.uni-trier.de/img/search.dark.16x16.png)
default search action
Aditi Raghunathan
Person information
Refine list
![note](https://dblp.uni-trier.de/img/note-mark.dark.12x12.png)
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
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) - [c32]Christina Baek, J. Zico Kolter, Aditi Raghunathan:
Why is SAM Robust to Label Noise? ICLR 2024 - [c31]Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan:
Understanding Catastrophic Forgetting in Language Models via Implicit Inference. ICLR 2024 - [c30]Pratyush Maini, Sachin Goyal, Zachary Chase Lipton, J. Zico Kolter, Aditi Raghunathan:
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. ICLR 2024 - [c29]Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan:
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. ICLR 2024 - [i46]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) - [i45]Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, Aditi Raghunathan:
Repetition Improves Language Model Embeddings. CoRR abs/2402.15449 (2024) - [i44]Taeyoun Kim, Suhas Kotha, Aditi Raghunathan:
Jailbreaking is Best Solved by Definition. CoRR abs/2403.14725 (2024) - [i43]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) - [i42]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) - [i41]Christina Baek, Zico Kolter, Aditi Raghunathan:
Why is SAM Robust to Label Noise? CoRR abs/2405.03676 (2024) - [i40]Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan:
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. CoRR abs/2405.20439 (2024) - [i39]Chen Henry Wu, Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried, Aditi Raghunathan:
Adversarial Attacks on Multimodal Agents. CoRR abs/2406.12814 (2024) - [i38]Gaurav R. Ghosal, Tatsunori Hashimoto, Aditi Raghunathan:
Understanding Finetuning for Factual Knowledge Extraction. CoRR abs/2406.14785 (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
aka: Zico Kolter
![](https://dblp.uni-trier.de/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-08-10 01:22 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint