Jacob Steinhardt

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 130

Top Venues

Publications

Danny Halawi, Alexander Wei, Eric Wallace, Tony T. Wang, Nika Haghtalab, Jacob Steinhardt
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation. CoRR (2024)
Yossi Gandelsman, Alexei A. Efros, Jacob Steinhardt
Interpreting the Second-Order Effects of Neurons in CLIP. CoRR (2024)
Yossi Gandelsman, Alexei A. Efros, Jacob Steinhardt
Interpreting CLIP's Image Representation via Text-Based Decomposition. ICLR (2024)
Erik Jones, Anca D. Dragan, Jacob Steinhardt
Adversaries Can Misuse Combinations of Safe Models. CoRR (2024)
Jiahai Feng, Jacob Steinhardt
How do Language Models Bind Entities in Context? ICLR (2024)
Alexander Pan, Erik Jones, Meena Jagadeesan, Jacob Steinhardt
Feedback Loops With Language Models Drive In-Context Reward Hacking. CoRR (2024)
Danny Halawi, Jean-Stanislas Denain, Jacob Steinhardt
Overthinking the Truth: Understanding how Language Models Process False Demonstrations. ICLR (2024)
Jiahai Feng, Stuart Russell, Jacob Steinhardt
Monitoring Latent World States in Language Models with Propositional Probes. CoRR (2024)
Danny Halawi, Fred Zhang, Chen Yueh-Han, Jacob Steinhardt
Approaching Human-Level Forecasting with Language Models. CoRR (2024)
Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt
Incentivizing High-Quality Content in Online Recommender Systems. CoRR (2023)
Alexander Wei, Nika Haghtalab, Jacob Steinhardt
Jailbroken: How Does LLM Safety Training Fail? NeurIPS (2023)
Yongyi Yang, Jacob Steinhardt, Wei Hu
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations. CoRR (2023)
Yongyi Yang, Jacob Steinhardt, Wei Hu
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations. ICML (2023)
Lisa Dunlap, Yuhui Zhang, Xiaohan Wang, Ruiqi Zhong, Trevor Darrell, Jacob Steinhardt, Joseph E. Gonzalez, Serena Yeung-Levy
Describing Differences in Image Sets with Natural Language. CoRR (2023)
Kevin Ro Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, Jacob Steinhardt
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small. ICLR (2023)
Yossi Gandelsman, Alexei A. Efros, Jacob Steinhardt
Interpreting CLIP's Image Representation via Text-Based Decomposition. CoRR (2023)
Shengbang Tong, Erik Jones, Jacob Steinhardt
Mass-Producing Failures of Multimodal Systems with Language Models. CoRR (2023)
Shengbang Tong, Erik Jones, Jacob Steinhardt
Mass-Producing Failures of Multimodal Systems with Language Models. NeurIPS (2023)
Danny Halawi, Jean-Stanislas Denain, Jacob Steinhardt
Overthinking the Truth: Understanding how Language Models Process False Demonstrations. CoRR (2023)
Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, Jacob Steinhardt
Eliciting Latent Predictions from Transformers with the Tuned Lens. CoRR (2023)
Jiahai Feng, Jacob Steinhardt
How do Language Models Bind Entities in Context? CoRR (2023)
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt
Progress measures for grokking via mechanistic interpretability. ICLR (2023)
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt
Progress measures for grokking via mechanistic interpretability. CoRR (2023)
Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt
Automatically Auditing Large Language Models via Discrete Optimization. ICML (2023)
Ruiqi Zhong, Peter Zhang, Steve Li, Jinwoo Ahn, Dan Klein, Jacob Steinhardt
Goal Driven Discovery of Distributional Differences via Language Descriptions. NeurIPS (2023)
Ruiqi Zhong, Peter Zhang, Steve Li, Jinwoo Ahn, Dan Klein, Jacob Steinhardt
Goal Driven Discovery of Distributional Differences via Language Descriptions. CoRR (2023)
Kush Bhatia, Wenshuo Guo, Jacob Steinhardt
Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws. AISTATS (2023)
Alexander Wei, Nika Haghtalab, Jacob Steinhardt
Jailbroken: How Does LLM Safety Training Fail? CoRR (2023)
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. CoRR (2023)
Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
Learning Equilibria in Matching Markets with Bandit Feedback. J. ACM 70 (3) (2023)
Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt
Automatically Auditing Large Language Models via Discrete Optimization. CoRR (2023)
Kush Bhatia, Wenshuo Guo, Jacob Steinhardt
Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws. CoRR (2023)
Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen R. McKeown
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. CoRR (2023)
Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt
Discovering Latent Knowledge in Language Models Without Supervision. ICLR (2023)
Meena Jagadeesan, Nikhil Garg, Jacob Steinhardt
Supply-Side Equilibria in Recommender Systems. NeurIPS (2023)
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. NeurIPS (2023)
Alexander Pan, Kush Bhatia, Jacob Steinhardt
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models. ICLR (2022)
Mantas Mazeika, Eric Tang, Andy Zou, Steven Basart, Jun Shern Chan, Dawn Song, David A. Forsyth, Jacob Steinhardt, Dan Hendrycks
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios. NeurIPS (2022)
Alexander Pan, Kush Bhatia, Jacob Steinhardt
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models. CoRR (2022)
Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt
Describing Differences between Text Distributions with Natural Language. ICML (2022)
Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks
Forecasting Future World Events with Neural Networks. CoRR (2022)
Erik Jones, Jacob Steinhardt
Capturing Failures of Large Language Models via Human Cognitive Biases. NeurIPS (2022)
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt
Predicting Out-of-Distribution Error with the Projection Norm. ICML (2022)
Meena Jagadeesan, Nikhil Garg, Jacob Steinhardt
Supply-Side Equilibria in Recommender Systems. CoRR (2022)
Alexander Wei, Wei Hu, Jacob Steinhardt
More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize. ICML (2022)
Pang Wei Koh, Jacob Steinhardt, Percy Liang
Stronger data poisoning attacks break data sanitization defenses. Mach. Learn. 111 (1) (2022)
Alexander Wei, Wei Hu, Jacob Steinhardt
More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize. CoRR (2022)
Mantas Mazeika, Eric Tang, Andy Zou, Steven Basart, Jun Shern Chan, Dawn Song, David A. Forsyth, Jacob Steinhardt, Dan Hendrycks
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios. CoRR (2022)
Ye Wang, Norman Mu, Daniele Grandi, Nicolas Savva, Jacob Steinhardt
A3D: Studying Pretrained Representations with Programmable Datasets. CVPR Workshops (2022)
Erik Jones, Jacob Steinhardt
Capturing Failures of Large Language Models via Human Cognitive Biases. CoRR (2022)
Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, Jacob Steinhardt
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small. CoRR (2022)
Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. CVPR (2022)
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt
Predicting Out-of-Distribution Error with the Projection Norm. CoRR (2022)
Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt
Summarizing Differences between Text Distributions with Natural Language. CoRR (2022)
Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt
Discovering Latent Knowledge in Language Models Without Supervision. CoRR (2022)
Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song
Scaling Out-of-Distribution Detection for Real-World Settings. ICML (2022)
Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks
Forecasting Future World Events With Neural Networks. NeurIPS (2022)
Jean-Stanislas Denain, Jacob Steinhardt
Auditing Visualizations: Transparency Methods Struggle to Detect Anomalous Behavior. CoRR (2022)
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt
Measuring Massive Multitask Language Understanding. ICLR (2021)
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt
Aligning AI With Shared Human Values. ICLR (2021)
Dan Hendrycks, Mantas Mazeika, Andy Zou, Sahil Patel, Christine Zhu, Jesus Navarro, Dawn Song, Bo Li, Jacob Steinhardt
What Would Jiminy Cricket Do? Towards Agents That Behave Morally. NeurIPS Datasets and Benchmarks (2021)
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt
Measuring Mathematical Problem Solving With the MATH Dataset. NeurIPS Datasets and Benchmarks (2021)
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. ICCV (2021)
Ruiqi Zhong, Dhruba Ghosh, Dan Klein, Jacob Steinhardt
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level. ACL/IJCNLP (Findings) (2021)
Ruiqi Zhong, Dhruba Ghosh, Dan Klein, Jacob Steinhardt
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level. CoRR (2021)
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt
Measuring Mathematical Problem Solving With the MATH Dataset. CoRR (2021)
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, Jacob Steinhardt
Measuring Coding Challenge Competence With APPS. NeurIPS Datasets and Benchmarks (2021)
Kush Bhatia, Peter L. Bartlett, Anca D. Dragan, Jacob Steinhardt
Agnostic learning with unknown utilities. CoRR (2021)
Alan Pham, Eunice Chan, Vikranth Srivatsa, Dhruba Ghosh, Yaoqing Yang, Yaodong Yu, Ruiqi Zhong, Joseph E. Gonzalez, Jacob Steinhardt
The Effect of Model Size on Worst-Group Generalization. CoRR (2021)
Kush Bhatia, Peter L. Bartlett, Anca D. Dragan, Jacob Steinhardt
Agnostic Learning with Unknown Utilities. ITCS (2021)
Collin Burns, Jacob Steinhardt
Limitations of Post-Hoc Feature Alignment for Robustness. CoRR (2021)
Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
Learning Equilibria in Matching Markets from Bandit Feedback. NeurIPS (2021)
Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt
Grounding Representation Similarity Through Statistical Testing. NeurIPS (2021)
Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt
Unsolved Problems in ML Safety. CoRR (2021)
Yaodong Yu, Zitong Yang, Edgar Dobriban, Jacob Steinhardt, Yi Ma
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. CoRR (2021)
Dan Hendrycks, Mantas Mazeika, Andy Zou, Sahil Patel, Christine Zhu, Jesus Navarro, Dawn Song, Bo Li, Jacob Steinhardt
What Would Jiminy Cricket Do? Towards Agents That Behave Morally. CoRR (2021)
Charlie Snell, Ruiqi Zhong, Dan Klein, Jacob Steinhardt
Approximating How Single Head Attention Learns. CoRR (2021)
Jacob Steinhardt
Technical perspective: Robust statistics tackle new problems. Commun. ACM 64 (5) (2021)
Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. CoRR (2021)
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song
Natural Adversarial Examples. CVPR (2021)
Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt
Grounding Representation Similarity with Statistical Testing. CoRR (2021)
Collin Burns, Jacob Steinhardt
Limitations of Post-Hoc Feature Alignment for Robustness. CVPR (2021)
Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
Learning Equilibria in Matching Markets from Bandit Feedback. CoRR (2021)
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, Jacob Steinhardt
Measuring Coding Challenge Competence With APPS. CoRR (2021)
Banghua Zhu, Jiantao Jiao, Jacob Steinhardt
When does the Tukey median work? 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)
Banghua Zhu, Jiantao Jiao, Jacob Steinhardt
When does the Tukey Median work? ISIT (2020)
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt
Measuring Massive Multitask Language Understanding. CoRR (2020)
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. ICML (2020)
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry
Identifying Statistical Bias in Dataset Replication. ICML (2020)
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry
Identifying Statistical Bias in Dataset Replication. CoRR (2020)
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. CoRR (2020)
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt
Aligning AI With Shared Human Values. 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)
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. CoRR (2020)
Banghua Zhu, Jiantao Jiao, Jacob Steinhardt
Robust estimation via generalized quasi-gradients. CoRR (2020)
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
Sever: A Robust Meta-Algorithm for Stochastic Optimization. ICML (2019)
Banghua Zhu, Jiantao Jiao, Jacob Steinhardt
Generalized Resilience and Robust Statistics. CoRR (2019)
Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, Jacob Steinhardt
Testing Robustness Against Unforeseen Adversaries. CoRR (2019)
Zachary C. Lipton, Jacob Steinhardt
Research for practice: troubling trends in machine-learning scholarship. Commun. ACM 62 (6) (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jacob Steinhardt