Henderson, Peter

35 publications

NeurIPS 2025 A Multimodal Benchmark for Framing of Oil & Gas Advertising and Potential Greenwashing Detection Gaku Morio, Harri Rowlands, Dominik Stammbach, Christopher D Manning, Peter Henderson

TMLR 2025 An Adversarial Perspective on Machine Unlearning for AI Safety Jakub Łucki, Boyi Wei, Yangsibo Huang, Peter Henderson, Florian Tramèr, Javier Rando

NeurIPS 2025 Dynamic Risk Assessments for Offensive Cybersecurity Agents Boyi Wei, Benedikt Stroebl, Jiacen Xu, Joie Zhang, Zhou Li, Peter Henderson

ICLR 2025 Fantastic Copyrighted Beasts and How (Not) to Generate Them Luxi He, Yangsibo Huang, Weijia Shi, Tinghao Xie, Haotian Liu, Yue Wang, Luke Zettlemoyer, Chiyuan Zhang, Danqi Chen, Peter Henderson

NeurIPS 2025 LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? Zihan Zheng, Zerui Cheng, Zeyu Shen, Shang Zhou, Kaiyuan Liu, Hansen He, Dongruixuan Li, Stanley Wei, Hangyi Hao, Jianzhu Yao, Peiyao Sheng, Zixuan Wang, Wenhao Chai, Aleksandra Korolova, Peter Henderson, Sanjeev Arora, Pramod Viswanath, Jingbo Shang, Saining Xie

ICLR 2025 On Evaluating the Durability of Safeguards for Open-Weight LLMs Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagielski, Milad Nasr, Prateek Mittal, Peter Henderson

ICML 2025 Position: In-House Evaluation Is Not Enough. Towards Robust Third-Party Evaluation and Flaw Disclosure for General-Purpose AI Shayne Longpre, Kevin Klyman, Ruth Elisabeth Appel, Sayash Kapoor, Rishi Bommasani, Michelle Sahar, Sean Mcgregor, Avijit Ghosh, Borhane Blili-Hamelin, Nathan Butters, Alondra Nelson, Dr. Amit Elazari, Andrew Sellars, Casey John Ellis, Dane Sherrets, Dawn Song, Harley Geiger, Ilona Cohen, Lauren Mcilvenny, Madhulika Srikumar, Mark M. Jaycox, Markus Anderljung, Nadine Farid Johnson, Nicholas Carlini, Nicolas Miailhe, Nik Marda, Peter Henderson, Rebecca S. Portnoff, Rebecca Weiss, Victoria Westerhoff, Yacine Jernite, Rumman Chowdhury, Percy Liang, Arvind Narayanan

ICLR 2025 SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, Prateek Mittal

ICLR 2025 Safety Alignment Should Be Made More than Just a Few Tokens Deep Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, Peter Henderson

NeurIPSW 2024 An Adversarial Perspective on Machine Unlearning for AI Safety Jakub Łucki, Boyi Wei, Yangsibo Huang, Peter Henderson, Florian Tramèr, Javier Rando

ICML 2024 Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, Peter Henderson

ICLRW 2024 Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, Peter Henderson

NeurIPS 2024 Evaluating Copyright Takedown Methods for Language Models Boyi Wei, Weijia Shi, Yangsibo Huang, Noah A. Smith, Chiyuan Zhang, Luke Zettlemoyer, Kai Li, Peter Henderson

ICLR 2024 Fine-Tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend to! Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, Peter Henderson

ICML 2024 Position: A Safe Harbor for AI Evaluation and Red Teaming Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Alex Pentland, Arvind Narayanan, Percy Liang, Peter Henderson

ICML 2024 Position: On the Societal Impact of Open Foundation Models Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Peter Cihon, Aspen K Hopkins, Kevin Bankston, Stella Biderman, Miranda Bogen, Rumman Chowdhury, Alex Engler, Peter Henderson, Yacine Jernite, Seth Lazar, Stefano Maffulli, Alondra Nelson, Joelle Pineau, Aviya Skowron, Dawn Song, Victor Storchan, Daniel Zhang, Daniel E. Ho, Percy Liang, Arvind Narayanan

TMLR 2024 The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Ifeoluwa Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini

AAAI 2024 Visual Adversarial Examples Jailbreak Aligned Large Language Models Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Peter Henderson, Mengdi Wang, Prateek Mittal

ICLRW 2024 What's in Your "Safe" Data?: Identifying Benign Data That Breaks Safety Luxi He, Mengzhou Xia, Peter Henderson

NeurIPS 2023 Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models Deepak Narayanan, Keshav Santhanam, Peter Henderson, Rishi Bommasani, Tony Lee, Percy Liang

AAAI 2023 Entropy Regularization for Population Estimation Ben Chugg, Peter Henderson, Jacob Goldin, Daniel E. Ho

JMLR 2023 Foundation Models and Fair Use Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang

TMLR 2023 Holistic Evaluation of Language Models Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D Manning, Christopher Re, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Andrew Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda

AAAI 2023 Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection Peter Henderson, Ben Chugg, Brandon R. Anderson, Kristen M. Altenburger, Alex Turk, John Guyton, Jacob Goldin, Daniel E. Ho

NeurIPS 2023 LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models Neel Guha, Julian Nyarko, Daniel Ho, Christopher Ré, Adam Chilton, Aditya K, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John Nay, Jonathan Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael Livermore, Nikon Rasumov-Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, Zehua Li

NeurIPS 2022 Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset Peter Henderson, Mark Krass, Lucia Zheng, Neel Guha, Christopher D Manning, Dan Jurafsky, Daniel Ho

ICMLW 2022 Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models Eric Mitchell, Peter Henderson, Christopher D Manning, Dan Jurafsky, Chelsea Finn

JMLR 2020 Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau

ICML 2019 Separating Value Functions Across Time-Scales Joshua Romoff, Peter Henderson, Ahmed Touati, Emma Brunskill, Joelle Pineau, Yann Ollivier

FnTML 2018 An Introduction to Deep Reinforcement Learning Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau

AAAI 2018 Deep Reinforcement Learning That Matters Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger

AAAI 2018 OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup

CoRL 2018 Reward Estimation for Variance Reduction in Deep Reinforcement Learning Joshua Romoff, Peter Henderson, Alexandre Piché, Vincent François-Lavet, Joelle Pineau

IJCAI 1971 Derived Semantics for Some Programming Language Constructs Peter Henderson