Peter Henderson

Orcid: 0000-0003-3938-0541

Affiliations:
  • Princeton University, Center For Information Technology Policy, NJ, USA
  • Stanford University, CA, USA (former, PhD 2023)
  • McGill University, Montreal, QC, Canada (former)


According to our database1, Peter Henderson authored at least 67 papers between 2015 and 2024.

Collaborative distances:

Timeline

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Bibliography

2024
Evaluating Copyright Takedown Methods for Language Models.
CoRR, 2024

The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources.
CoRR, 2024

SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors.
CoRR, 2024

Fantastic Copyrighted Beasts and How (Not) to Generate Them.
CoRR, 2024

Safety Alignment Should Be Made More Than Just a Few Tokens Deep.
CoRR, 2024

JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model Edits.
CoRR, 2024

AI Risk Management Should Incorporate Both Safety and Security.
CoRR, 2024

FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data Mixtures for Legal Reasoning.
CoRR, 2024

What's in Your "Safe" Data?: Identifying Benign Data that Breaks Safety.
CoRR, 2024

On the Societal Impact of Open Foundation Models.
CoRR, 2024

A Safe Harbor for AI Evaluation and Red Teaming.
CoRR, 2024

Promises and pitfalls of artificial intelligence for legal applications.
CoRR, 2024

Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications.
Proceedings of the Forty-first International Conference on Machine Learning, 2024



Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct.
Proceedings of the Symposium on Computer Science and Law, 2024

Visual Adversarial Examples Jailbreak Aligned Large Language Models.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Holistic Evaluation of Language Models.
Trans. Mach. Learn. Res., 2023

Foundation Models and Fair Use.
J. Mach. Learn. Res., 2023

LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.
CoRR, 2023

Freedom of Speech and AI Output.
CoRR, 2023

Where's the Liability in Harmful AI Speech?
CoRR, 2023

Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs.
CoRR, 2023

Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023


Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models.
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

Entropy Regularization for Population Estimation.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Text Characterization Toolkit.
CoRR, 2022

Data Governance in the Age of Large-Scale Data-Driven Language Technology.
CoRR, 2022

Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Text Characterization Toolkit (TCT).
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, 2022

Data Governance in the Age of Large-Scale Data-Driven Language Technology.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy.
Proceedings of the 2022 Symposium on Computer Science and Law, 2022

2021
Beyond Ads: Sequential Decision-Making Algorithms in Public Policy.
CoRR, 2021

On the Opportunities and Risks of Foundation Models.
CoRR, 2021

When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset.
CoRR, 2021

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning.
CoRR, 2021

When does pretraining help?: assessing self-supervised learning for law and the CaseHOLD dataset of 53, 000+ legal holdings.
Proceedings of the ICAIL '21: Eighteenth International Conference for Artificial Intelligence and Law, São Paulo Brazil, June 21, 2021

TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning?
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020
Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop.
CoRR, 2020

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?
CoRR, 2020

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims.
CoRR, 2020

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning.
CoRR, 2020

With Little Power Comes Great Responsibility.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2019
Separating value functions across time-scales.
CoRR, 2019

Separable value functions across time-scales.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
An Introduction to Deep Reinforcement Learning.
Found. Trends Mach. Learn., 2018

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version.
Dialogue Discourse, 2018

Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research.
CoRR, 2018

The RLLChatbot: a solution to the ConvAI challenge.
CoRR, 2018

Adversarial Gain.
CoRR, 2018

Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods.
CoRR, 2018

Cost Adaptation for Robust Decentralized Swarm Behaviour.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Reward Estimation for Variance Reduction in Deep Reinforcement Learning.
Proceedings of the 6th International Conference on Learning Representations, 2018

Ethical Challenges in Data-Driven Dialogue Systems.
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

Deep Reinforcement Learning That Matters.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Learning Robust Dialog Policies in Noisy Environments.
CoRR, 2017

Bayesian Policy Gradients via Alpha Divergence Dropout Inference.
CoRR, 2017

Benchmark Environments for Multitask Learning in Continuous Domains.
CoRR, 2017

Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control.
CoRR, 2017

An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models.
CoRR, 2017

Underwater multi-robot convoying using visual tracking by detection.
Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017

2016
Chaotic Memory Randomization for Securing Embedded Systems.
CoRR, 2016

2015
A Survey of Available Corpora for Building Data-Driven Dialogue Systems.
CoRR, 2015


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