On Coloring the Odd-Distance Graph. - Jacob Steinhardt (Electron. J. Comb., 2009)

Permutations with Ascending and Descending Blocks. - Jacob Steinhardt (Electron. J. Comb., 2010)

Finite-Time Regional Verification of Stochastic Nonlinear Systems. - Jacob Steinhardt; Russ Tedrake (Robotics: Science and Systems, 2011)

Finite-time regional verification of stochastic non-linear systems. - Jacob Steinhardt; Russ Tedrake (Int. J. Robotics Res., 2012)

Flexible Martingale Priors for Deep Hierarchies. - Jacob Steinhardt; Zoubin Ghahramani (AISTATS, 2012)

Filtering with Abstract Particles. - Jacob Steinhardt; Percy Liang (ICML, 2014)

Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm. - Jacob Steinhardt; Percy Liang (ICML, 2014)

The Statistics of Streaming Sparse Regression. - Jacob Steinhardt; Stefan Wager; Percy Liang (CoRR, 2014)

Learning Where to Sample in Structured Prediction. - Tianlin Shi; Jacob Steinhardt; Percy Liang (AISTATS, 2015)

Minimax rates for memory-bounded sparse linear regression. - Jacob Steinhardt; John C. Duchi (COLT, 2015)

Reified Context Models. - Jacob Steinhardt; Percy Liang (ICML, 2015)

Learning Fast-Mixing Models for Structured Prediction. - Jacob Steinhardt; Percy Liang (ICML, 2015)

Learning with Relaxed Supervision. - Jacob Steinhardt; Percy Liang (NIPS, 2015)

Memory, Communication, and Statistical Queries. - Jacob Steinhardt; Gregory Valiant; Stefan Wager (Electron. Colloquium Comput. Complex., 2015)

Unsupervised Risk Estimation Using Only Conditional Independence Structure. - Jacob Steinhardt; Percy Liang (NIPS, 2016)

Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction. - Jacob Steinhardt; Gregory Valiant; Moses Charikar (NIPS, 2016)

Concrete Problems in AI Safety. - Dario Amodei; Chris Olah; Jacob Steinhardt; Paul F. Christiano; John Schulman; Dan Mané (CoRR, 2016)

Certified Defenses for Data Poisoning Attacks. - Jacob Steinhardt; Pang Wei Koh; Percy Liang (NIPS, 2017)

Learning from untrusted data. - Moses Charikar; Jacob Steinhardt; Gregory Valiant (STOC, 2017)

Better Agnostic Clustering Via Relaxed Tensor Norms. - Pravesh K. Kothari; Jacob Steinhardt (CoRR, 2017)

Does robustness imply tractability? A lower bound for planted clique in the semi-random model. - Jacob Steinhardt (Electron. Colloquium Comput. Complex., 2017)

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

Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers. - Jacob Steinhardt; Moses Charikar; Gregory Valiant (ITCS, 2018)

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

Robust moment estimation and improved clustering via sum of squares. - Pravesh K. Kothari; Jacob Steinhardt; David Steurer (STOC, 2018)

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. - Miles Brundage; Shahar Avin; Jack Clark; Helen Toner; Peter Eckersley; Ben Garfinkel; Allan Dafoe; Paul Scharre; Thomas Zeitzoff; Bobby Filar; Hyrum S. Anderson; Heather Roff; et al. (CoRR, 2018)

Robust learning: information theory and algorithms. - Jacob Steinhardt (2018)

Sever: A Robust Meta-Algorithm for Stochastic Optimization. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Jacob Steinhardt; Alistair Stewart (ICML, 2019)

Research for practice: troubling trends in machine-learning scholarship. - Zachary C. Lipton; Jacob Steinhardt (Commun. ACM, 2019)

Transfer of Adversarial Robustness Between Perturbation Types. - Daniel Kang; Yi Sun 010; Tom Brown; Dan Hendrycks; Jacob Steinhardt (CoRR, 2019)

Testing Robustness Against Unforeseen Adversaries. - Daniel Kang; Yi Sun 010; Dan Hendrycks; Tom Brown; Jacob Steinhardt (CoRR, 2019)

Generalized Resilience and Robust Statistics. - Banghua Zhu; Jiantao Jiao; Jacob Steinhardt (CoRR, 2019)

A Benchmark for Anomaly Segmentation. - Dan Hendrycks; Steven Basart; Mantas Mazeika; Mohammadreza Mostajabi; Jacob Steinhardt; Dawn Song (CoRR, 2019)

FrAngel: component-based synthesis with control structures. - Kensen Shi; Jacob Steinhardt; Percy Liang (Proc. ACM Program. Lang., 2019)

Troubling Trends in Machine Learning Scholarship. - Zachary C. Lipton; Jacob Steinhardt (ACM Queue, 2019)

Identifying Statistical Bias in Dataset Replication. - Logan Engstrom; Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Jacob Steinhardt; Aleksander Madry (ICML, 2020)

Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. - Zitong Yang; Yaodong Yu; Chong You; Jacob Steinhardt; Yi Ma 001 (ICML, 2020)

When does the Tukey Median work? - Banghua Zhu; Jiantao Jiao; Jacob Steinhardt (ISIT, 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)

Robust estimation via generalized quasi-gradients. - Banghua Zhu; Jiantao Jiao; Jacob Steinhardt (CoRR, 2020)

Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level. - Ruiqi Zhong; Dhruba Ghosh; Dan Klein 001; Jacob Steinhardt (ACL/IJCNLP (Findings), 2021)

Limitations of Post-Hoc Feature Alignment for Robustness. - Collin Burns; Jacob Steinhardt (CVPR, 2021)

Natural Adversarial Examples. - Dan Hendrycks; Kevin Zhao; Steven Basart; Jacob Steinhardt; Dawn Song (CVPR, 2021)

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. - Dan Hendrycks; Steven Basart; Norman Mu; Saurav Kadavath; Frank Wang; Evan Dorundo; Rahul Desai; Tyler Zhu; Samyak Parajuli; Mike Guo; Dawn Song; Jacob Steinhardt; et al. (ICCV, 2021)

Aligning AI With Shared Human Values. - Dan Hendrycks; Collin Burns; Steven Basart; Andrew Critch; Jerry Li 001; Dawn Song; Jacob Steinhardt (ICLR, 2021)

Measuring Massive Multitask Language Understanding. - Dan Hendrycks; Collin Burns; Steven Basart; Andy Zou; Mantas Mazeika; Dawn Song; Jacob Steinhardt (ICLR, 2021)

Agnostic Learning with Unknown Utilities. - Kush Bhatia; Peter L. Bartlett; Anca D. Dragan; Jacob Steinhardt (ITCS, 2021)

Grounding Representation Similarity Through Statistical Testing. - Frances Ding; Jean-Stanislas Denain; Jacob Steinhardt (NeurIPS, 2021)

Measuring Mathematical Problem Solving With the MATH Dataset. - Dan Hendrycks; Collin Burns; Saurav Kadavath; Akul Arora; Steven Basart; Eric Tang; Dawn Song; Jacob Steinhardt (NeurIPS Datasets and Benchmarks, 2021)

Measuring Coding Challenge Competence With APPS. - Dan Hendrycks; Steven Basart; Saurav Kadavath; Mantas Mazeika; Akul Arora; Ethan Guo; Collin Burns; Samir Puranik; Horace He; Dawn Song; Jacob Steinhardt (NeurIPS Datasets and Benchmarks, 2021)

What Would Jiminy Cricket Do? Towards Agents That Behave Morally. - Dan Hendrycks; Mantas Mazeika; Andy Zou; Sahil Patel; Christine Zhu; Jesus Navarro; Dawn Song; Bo Li 026; Jacob Steinhardt (NeurIPS Datasets and Benchmarks, 2021)

Learning Equilibria in Matching Markets from Bandit Feedback. - Meena Jagadeesan; Alexander Wei 001; Yixin Wang 002; Michael I. Jordan; Jacob Steinhardt (NeurIPS, 2021)

Technical perspective: Robust statistics tackle new problems. - Jacob Steinhardt (Commun. ACM, 2021)

Approximating How Single Head Attention Learns. - Charlie Snell; Ruiqi Zhong; Dan Klein 001; Jacob Steinhardt (CoRR, 2021)

Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. - Yaodong Yu; Zitong Yang; Edgar Dobriban; Jacob Steinhardt; Yi Ma 001 (CoRR, 2021)

Grounding Representation Similarity with Statistical Testing. - Frances Ding; Jean-Stanislas Denain; Jacob Steinhardt (CoRR, 2021)

Unsolved Problems in ML Safety. - Dan Hendrycks; Nicholas Carlini; John Schulman; Jacob Steinhardt (CoRR, 2021)

The Effect of Model Size on Worst-Group Generalization. - Alan Pham; Eunice Chan; Vikranth Srivatsa; Dhruba Ghosh; Yaoqing Yang; Yaodong Yu; Ruiqi Zhong; Joseph E. Gonzalez; Jacob Steinhardt (CoRR, 2021)

PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. - Dan Hendrycks; Andy Zou; Mantas Mazeika; Leonard Tang; Bo Li 026; Dawn Song; Jacob Steinhardt (CVPR, 2022)

A3D: Studying Pretrained Representations with Programmable Datasets. - Ye Wang; Norman Mu; Daniele Grandi; Nicolas Savva; Jacob Steinhardt (CVPR Workshops, 2022)

The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models. - Alexander Pan; Kush Bhatia; Jacob Steinhardt (ICLR, 2022)

More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize. - Alexander Wei 001; Wei Hu; Jacob Steinhardt (ICML, 2022)

Scaling Out-of-Distribution Detection for Real-World Settings. - Dan Hendrycks; Steven Basart; Mantas Mazeika; Andy Zou; Joseph Kwon; Mohammadreza Mostajabi; Jacob Steinhardt; Dawn Song (ICML, 2022)

Predicting Out-of-Distribution Error with the Projection Norm. - Yaodong Yu; Zitong Yang; Alexander Wei 001; Yi Ma 001; Jacob Steinhardt (ICML, 2022)

Describing Differences between Text Distributions with Natural Language. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jacob Steinhardt (ICML, 2022)

Capturing Failures of Large Language Models via Human Cognitive Biases. - Erik Jones; Jacob Steinhardt (NeurIPS, 2022)

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios. - Mantas Mazeika; Eric Tang; Andy Zou; Steven Basart; Jun Shern Chan; Dawn Song; David A. Forsyth; Jacob Steinhardt; Dan Hendrycks (NeurIPS, 2022)

Forecasting Future World Events With Neural Networks. - Andy Zou; Tristan Xiao; Ryan Jia; Joe Kwon; Mantas Mazeika; Richard Li; Dawn Song; Jacob Steinhardt; Owain Evans; Dan Hendrycks (NeurIPS, 2022)

Summarizing Differences between Text Distributions with Natural Language. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jacob Steinhardt (CoRR, 2022)

Auditing Visualizations: Transparency Methods Struggle to Detect Anomalous Behavior. - Jean-Stanislas Denain; Jacob Steinhardt (CoRR, 2022)

Stronger data poisoning attacks break data sanitization defenses. - Pang Wei Koh; Jacob Steinhardt; Percy Liang (Mach. Learn., 2022)

Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws. - Kush Bhatia; Wenshuo Guo; Jacob Steinhardt (AISTATS, 2023)

Discovering Latent Knowledge in Language Models Without Supervision. - Collin Burns; Haotian Ye; Dan Klein 001; Jacob Steinhardt (ICLR, 2023)

Progress measures for grokking via mechanistic interpretability. - Neel Nanda; Lawrence Chan; Tom Lieberum; Jess Smith; Jacob Steinhardt (ICLR, 2023)

Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small. - Kevin Ro Wang; Alexandre Variengien; Arthur Conmy; Buck Shlegeris; Jacob Steinhardt (ICLR, 2023)

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

Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations. - Yongyi Yang; Jacob Steinhardt; Wei Hu (ICML, 2023)

Jailbroken: How Does LLM Safety Training Fail? - Alexander Wei 001; Nika Haghtalab; Jacob Steinhardt (NeurIPS, 2023)

Supply-Side Equilibria in Recommender Systems. - Meena Jagadeesan; Nikhil Garg 001; Jacob Steinhardt (NeurIPS, 2023)

Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. - Meena Jagadeesan; Michael I. Jordan; Jacob Steinhardt; Nika Haghtalab (NeurIPS, 2023)

Mass-Producing Failures of Multimodal Systems with Language Models. - Shengbang Tong; Erik Jones; Jacob Steinhardt (NeurIPS, 2023)

Goal Driven Discovery of Distributional Differences via Language Descriptions. - Ruiqi Zhong; Peter Zhang; Steve Li; Jinwoo Ahn; Dan Klein 001; Jacob Steinhardt (NeurIPS, 2023)

Eliciting Latent Predictions from Transformers with the Tuned Lens. - Nora Belrose; Zach Furman; Logan Smith; Danny Halawi; Igor Ostrovsky; Lev McKinney; Stella Biderman; Jacob Steinhardt (CoRR, 2023)

Incentivizing High-Quality Content in Online Recommender Systems. - Xinyan Hu; Meena Jagadeesan; Michael I. Jordan; Jacob Steinhardt (CoRR, 2023)

Learning Equilibria in Matching Markets with Bandit Feedback. - Meena Jagadeesan; Alexander Wei 001; Yixin Wang 002; Michael I. Jordan; Jacob Steinhardt (J. ACM, 2023)

Describing Differences in Image Sets with Natural Language. - Lisa Dunlap; Yuhui Zhang; Xiaohan Wang; Ruiqi Zhong; Trevor Darrell; Jacob Steinhardt; Joseph E. Gonzalez; Serena Yeung-Levy (CVPR, 2024)

How do Language Models Bind Entities in Context? - Jiahai Feng; Jacob Steinhardt (ICLR, 2024)

Interpreting CLIP's Image Representation via Text-Based Decomposition. - Yossi Gandelsman; Alexei A. Efros; Jacob Steinhardt (ICLR, 2024)

Overthinking the Truth: Understanding how Language Models Process False Demonstrations. - Danny Halawi; Jean-Stanislas Denain; Jacob Steinhardt (ICLR, 2024)

Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. - Yanda Chen; Ruiqi Zhong; Narutatsu Ri; Chen Zhao; He He 001; Jacob Steinhardt; Zhou Yu; Kathleen R. McKeown (ICML, 2024)

Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation. - Danny Halawi; Alexander Wei 001; Eric Wallace; Tony Tong Wang; Nika Haghtalab; Jacob Steinhardt (ICML, 2024)

Feedback Loops With Language Models Drive In-Context Reward Hacking. - Alexander Pan; Erik Jones; Meena Jagadeesan; Jacob Steinhardt (ICML, 2024)

Approaching Human-Level Forecasting with Language Models. - Danny Halawi; Fred Zhang; Chen Yueh-Han; Jacob Steinhardt (NeurIPS, 2024)

Explaining Datasets in Words: Statistical Models with Natural Language Parameters. - Ruiqi Zhong; Heng Wang; Dan Klein 001; Jacob Steinhardt (NeurIPS, 2024)

Adversaries Can Misuse Combinations of Safe Models. - Erik Jones; Anca D. Dragan; Jacob Steinhardt (CoRR, 2024)

Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry. - Meena Jagadeesan; Michael I. Jordan; Jacob Steinhardt (CoRR, 2024)

What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? - Katie Kang; Amrith Setlur; Dibya Ghosh; Jacob Steinhardt; Claire J. Tomlin; Sergey Levine; Aviral Kumar (CoRR, 2024)

Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts. - Jiahai Feng; Stuart Russell 001; Jacob Steinhardt (CoRR, 2024)

LatentQA: Teaching LLMs to Decode Activations Into Natural Language. - Alexander Pan; Lijie Chen 001; Jacob Steinhardt (CoRR, 2024)

VibeCheck: Discover and Quantify Qualitative Differences in Large Language Models. - Lisa Dunlap; Krishna Mandal; Trevor Darrell; Jacob Steinhardt; Joseph E. Gonzalez (ICLR, 2025)

Monitoring Latent World States in Language Models with Propositional Probes. - Jiahai Feng; Stuart Russell 001; Jacob Steinhardt (ICLR, 2025)

Interpreting the Second-Order Effects of Neurons in CLIP. - Yossi Gandelsman; Alexei A. Efros; Jacob Steinhardt (ICLR, 2025)

Uncovering Gaps in How Humans and LLMs Interpret Subjective Language. - Erik Jones; Arjun Patrawala; Jacob Steinhardt (ICLR, 2025)

Language Models Learn to Mislead Humans via RLHF. - Jiaxin Wen; Ruiqi Zhong; Akbir Khan; Ethan Perez; Jacob Steinhardt; Minlie Huang; Samuel R. Bowman; He He 001; Shi Feng 005 (ICLR, 2025)

Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision. - Yaowen Ye; Cassidy Laidlaw; Jacob Steinhardt (ICLR, 2025)

Eliciting Language Model Behaviors with Investigator Agents. - Xiang Lisa Li; Neil Chowdhury; Daniel D. Johnson 001; Tatsunori Hashimoto; Percy Liang; Sarah Schwettmann; Jacob Steinhardt (CoRR, 2025)

Which Attention Heads Matter for In-Context Learning? - Kayo Yin; Jacob Steinhardt (CoRR, 2025)

Understanding In-context Learning of Addition via Activation Subspaces. - Xinyan Hu; Kayo Yin; Michael I. Jordan; Jacob Steinhardt; Lijie Chen 001 (CoRR, 2025)

Establishing Best Practices for Building Rigorous Agentic Benchmarks. - Yuxuan Zhu; Tengjun Jin; Yada Pruksachatkun; Andy Zhang; Shu Liu; Sasha Cui; Sayash Kapoor; Shayne Longpre; Kevin Meng; Rebecca Weiss; Fazl Barez; Rahul Gupta 001; et al. (CoRR, 2025)