Steinhardt, Jacob

Concrete Problems in AI Safety

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 1,314

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 1,250

Natural Adversarial Examples

Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., & Song, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 1,090

Natural Adversarial Examples

Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., & Song, D. (2019) · arXiv

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 1,058

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 849

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · Apollo - University of Cambridge Repository

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 849

Aligning AI With Shared Human Values

Hendrycks, D., Burns, C., Basart, S., Critch, A., Li, J., Song, D., & Steinhardt, J. (2020) · arXiv

Ethics and Social Impacts of AIPsychology of Moral and Emotional Judgment

Cited by 517

Certified Defenses for Data Poisoning Attacks

Steinhardt, J., Koh, P. W., & Liang, P. (2017) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 479

Measuring Mathematical Problem Solving With the MATH Dataset

Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021) · arXiv

Topic ModelingMathematics, Computing, and Information Processing

Cited by 306

Measuring Massive Multitask Language Understanding

Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2020) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 291

Jailbroken: How Does LLM Safety Training Fail?

Wei, A., Haghtalab, N., & Steinhardt, J. (2023) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 280

Jailbroken: How Does LLM Safety Training Fail?

Wei, A., Haghtalab, N., & Steinhardt, J. (2023) · Advances in Neural Information Processing Systems 36

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 280

Troubling Trends in Machine Learning Scholarship: Some ML papers suffer from flaws that could mislead the public and stymie future research.

Lipton, Z. C., & Steinhardt, J. (2019) · Queue

Academic Writing and PublishingAcademic Publishing and Open Access

Cited by 231

Stronger Data Poisoning Attacks Break Data Sanitization Defenses

Koh, P. W., Steinhardt, J., & Liang, P. (2018) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 189

Measuring Coding Challenge Competence With APPS

Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 142

Unsolved Problems in ML Safety

Hendrycks, D., Carlini, N., Schulman, J., & Steinhardt, J. (2021) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 95

Protein language models are biased by unequal sequence sampling across the tree of life

Ding, F., & Steinhardt, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 57

Discovering Latent Knowledge in Language Models Without Supervision

Burns, C., Ye, H., Klein, D., & Steinhardt, J. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 55

Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

Wang, K., Variengien, A., Conmy, A., Shlegeris, B., & Steinhardt, J. (2022) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 52

Progress measures for grokking via mechanistic interpretability

Nanda, N., Chan, L., Lieberum, T., Smith, J., & Steinhardt, J. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Formal Methods in Verification

Cited by 51

Capturing Failures of Large Language Models Via Human Cognitive Biases

Jones, E., & Steinhardt, J. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingNatural Language Processing Techniques

Cited by 48

Capturing Failures of Large Language Models via Human Cognitive Biases

Jones, E., & Steinhardt, J. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 43

Eliciting Latent Predictions from Transformers with the Tuned Lens

Belrose, N., Ostrovsky, I., McKinney, L., Furman, Z., Smith, L., Halawi, D., Biderman, S., & Steinhardt, J. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 27

The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models

Pan, A., Bhatia, K., & Steinhardt, J. (2022) · arXiv

Reinforcement Learning in RoboticsAdversarial Robustness in Machine Learning

Cited by 25