Jacob Steinhardt

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Dan HendrycksDirector of the Center for AI Safety (advisor for xAI and Scale)Dirección de correo verificada de berkeley.edu
Dawn SongProfessor of Computer Science, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Steven BasartPhD, University of ChicagoDirección de correo verificada de ttic.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Christopher OlahAnthropicDirección de correo verificada de google.com
John SchulmanAnthropicDirección de correo verificada de anthropic.com
Dario AmodeiCEO and Co-Founder at AnthropicDirección de correo verificada de anthropic.com
Aditi RaghunathanAssistant professor, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Paul ChristianoNational Institute of Standards and TechnologyDirección de correo verificada de nist.gov
Gregory ValiantAssistant Professor of Computer Science, Stanford UniversityDirección de correo verificada de stanford.edu
Pang Wei KohUniversity of WashingtonDirección de correo verificada de cs.washington.edu
Zachary C. LiptonRaj Reddy Associate Professor of Machine Learning @ Carnegie Mellon University; CTO + CSO @ AbridgeDirección de correo verificada de cmu.edu
Moses CharikarProfessor of Computer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Jerry LiUniversity of WashingtonDirección de correo verificada de cs.washington.edu
Daniel KangUIUCDirección de correo verificada de illinois.edu
Tom B BrownAnthropicDirección de correo verificada de anthropic.com
Andrew IlyasMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Pravesh K. KothariPrinceton UniversityDirección de correo verificada de cs.cmu.edu
Banghua ZhuUniversity of California, BerkeleyDirección de correo verificada de berkeley.edu
Jiantao JiaoAssistant Professor of EECS and Statistics, University of California, BerkeleyDirección de correo verificada de berkeley.edu

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Jacob Steinhardt

Stanford University

Dirección de correo verificada de cs.stanford.edu - Página principal

Machine learning Statistics


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Concrete problems in AI safety D Amodei, C Olah, J Steinhardt, P Christiano, J Schulman, D Mané arXiv preprint arXiv:1606.06565, 2016	3131	2016
Measuring massive multitask language understanding D Hendrycks, C Burns, S Basart, A Zou, M Mazeika, D Song, J Steinhardt arXiv preprint arXiv:2009.03300, 2020	3010	2020
The many faces of robustness: A critical analysis of out-of-distribution generalization D Hendrycks, S Basart, N Mu, S Kadavath, F Wang, E Dorundo, R Desai, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2021	1796	2021
Natural adversarial examples D Hendrycks, K Zhao, S Basart, J Steinhardt, D Song Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021	1598	2021
Measuring mathematical problem solving with the math dataset D Hendrycks, C Burns, S Kadavath, A Arora, S Basart, E Tang, D Song, ... arXiv preprint arXiv:2103.03874, 2021	1261	2021
The malicious use of artificial intelligence: Forecasting, prevention, and mitigation M Brundage, S Avin, J Clark, H Toner, P Eckersley, B Garfinkel, A Dafoe, ... arXiv preprint arXiv:1802.07228, 2018	1206	2018
Certified defenses against adversarial examples A Raghunathan, J Steinhardt, P Liang arXiv preprint arXiv:1801.09344, 2018	1154	2018
Certified defenses for data poisoning attacks J Steinhardt, PWW Koh, PS Liang Advances in neural information processing systems 30, 2017	952	2017
Jailbroken: How does llm safety training fail? A Wei, N Haghtalab, J Steinhardt Advances in Neural Information Processing Systems 36, 2024	759	2024
Measuring coding challenge competence with apps D Hendrycks, S Basart, S Kadavath, M Mazeika, A Arora, E Guo, C Burns, ... arXiv preprint arXiv:2105.09938, 2021	545	2021
Semidefinite relaxations for certifying robustness to adversarial examples A Raghunathan, J Steinhardt, PS Liang Advances in neural information processing systems 31, 2018	514	2018
Scaling out-of-distribution detection for real-world settings D Hendrycks, S Basart, M Mazeika, A Zou, J Kwon, M Mostajabi, ... arXiv preprint arXiv:1911.11132, 2019	489	2019
Aligning ai with shared human values D Hendrycks, C Burns, S Basart, A Critch, J Li, D Song, J Steinhardt arXiv preprint arXiv:2008.02275, 2020	474	2020
Troubling Trends in Machine Learning Scholarship: Some ML papers suffer from flaws that could mislead the public and stymie future research. ZC Lipton, J Steinhardt Queue 17 (1), 45-77, 2019	392	2019
Interpretability in the wild: a circuit for indirect object identification in gpt-2 small K Wang, A Variengien, A Conmy, B Shlegeris, J Steinhardt arXiv preprint arXiv:2211.00593, 2022	385	2022
Sever: A robust meta-algorithm for stochastic optimization I Diakonikolas, G Kamath, D Kane, J Li, J Steinhardt, A Stewart International Conference on Machine Learning, 1596-1606, 2019	349	2019
Learning from untrusted data M Charikar, J Steinhardt, G Valiant Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017	349	2017
Sonyc: A system for monitoring, analyzing, and mitigating urban noise pollution JP Bello, C Silva, O Nov, RL Dubois, A Arora, J Salamon, C Mydlarz, ... Communications of the ACM 62 (2), 68-77, 2019	345	2019
Unsolved problems in ml safety D Hendrycks, N Carlini, J Schulman, J Steinhardt arXiv preprint arXiv:2109.13916, 2021	343	2021
Progress measures for grokking via mechanistic interpretability N Nanda, L Chan, T Lieberum, J Smith, J Steinhardt arXiv preprint arXiv:2301.05217, 2023	335	2023

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