Peter Henderson

research

∙ 08/20/2023

LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

The advent of large language models (LLMs) and their adoption by the leg...

0 Neel Guha, et al. ∙

research

∙ 08/16/2023

Freedom of Speech and AI Output

Is the output of generative AI entitled to First Amendment protection? W...

0 eugene-volokh, et al. ∙

research

∙ 08/09/2023

Where's the Liability in Harmful AI Speech?

Generative AI, in particular text-based "foundation models" (large model...

0 Peter Henderson, et al. ∙

research

∙ 05/03/2023

Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs

Large language models (LLMs) power many state-of-the-art systems in natu...

0 Deepak Narayanan, et al. ∙

research

∙ 03/28/2023

Foundation Models and Fair Use

Existing foundation models are trained on copyrighted material. Deployin...

0 Peter Henderson, et al. ∙

research

∙ 11/27/2022

Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models

A growing ecosystem of large, open-source foundation models has reduced ...

0 Eric Mitchell, et al. ∙

research

∙ 11/16/2022

Holistic Evaluation of Language Models

Language models (LMs) are becoming the foundation for almost all major l...

21 Percy Liang, et al. ∙

research

∙ 10/04/2022

Text Characterization Toolkit

In NLP, models are usually evaluated by reporting single-number performa...

0 Daniel Simig, et al. ∙

research

∙ 08/24/2022

Entropy Regularization for Population Estimation

Entropy regularization is known to improve exploration in sequential dec...

0 Ben Chugg, et al. ∙

research

∙ 07/01/2022

Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset

One concern with the rise of large language models lies with their poten...

0 Peter Henderson, et al. ∙

research

∙ 05/04/2022

Data Governance in the Age of Large-Scale Data-Driven Language Technology

The recent emergence and adoption of Machine Learning technology, and sp...

0 Yacine Jernite, et al. ∙

research

∙ 04/25/2022

Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection

We introduce a new setting, optimize-and-estimate structured bandits. He...

0 Peter Henderson, et al. ∙

research

∙ 12/13/2021

Beyond Ads: Sequential Decision-Making Algorithms in Public Policy

We explore the promises and challenges of employing sequential decision-...

0 Peter Henderson, et al. ∙

research

∙ 04/18/2021

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

While self-supervised learning has made rapid advances in natural langua...

0 Lucia Zheng, et al. ∙

research

∙ 03/10/2021

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning

How do we formalize the challenge of credit assignment in reinforcement ...

0 Dilip Arumugam, et al. ∙

research

∙ 10/13/2020

With Little Power Comes Great Responsibility

Despite its importance to experimental design, statistical power (the pr...

5 Dallas Card, et al. ∙

research

∙ 07/21/2020

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

This report documents ideas for improving the field of machine learning,...

44 Shagun Sodhani, et al. ∙

research

∙ 07/06/2020

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

We investigate whether Jacobi preconditioning, accounting for the bootst...

0 Joshua Romoff, et al. ∙

research

∙ 04/15/2020

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

With the recent wave of progress in artificial intelligence (AI) has com...

0 Miles Brundage, et al. ∙

research

∙ 01/31/2020

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Accurate reporting of energy and carbon usage is essential for understan...

0 Peter Henderson, et al. ∙

research

∙ 02/05/2019

Separating value functions across time-scales

In many finite horizon episodic reinforcement learning (RL) settings, it...

0 Joshua Romoff, et al. ∙

research

∙ 12/03/2018

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

The current flood of information in all areas of machine learning resear...

0 Peter Henderson, et al. ∙

research

∙ 11/30/2018

An Introduction to Deep Reinforcement Learning

Deep reinforcement learning is the combination of reinforcement learning...

28 Vincent Francois-Lavet, et al. ∙

research

∙ 11/07/2018

The RLLChatbot: a solution to the ConvAI Challenge

Current conversational systems can follow simple commands and answer bas...

0 Nicolas A. Gontier, et al. ∙

research

∙ 11/04/2018

Adversarial Gain

Adversarial examples can be defined as inputs to a model which induce a ...

0 Peter Henderson, et al. ∙

research

∙ 10/05/2018

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

Recent analyses of certain gradient descent optimization methods have sh...

12 Peter Henderson, et al. ∙

research

∙ 05/09/2018

Reward Estimation for Variance Reduction in Deep Reinforcement Learning

In reinforcement learning (RL), stochastic environments can make learnin...

0 Joshua Romoff, et al. ∙

research

∙ 12/11/2017

Learning Robust Dialog Policies in Noisy Environments

Modern virtual personal assistants provide a convenient interface for co...

0 Maryam Fazel-Zarandi, et al. ∙

research

∙ 11/24/2017

Ethical Challenges in Data-Driven Dialogue Systems

The use of dialogue systems as a medium for human-machine interaction is...

0 Peter Henderson, et al. ∙

research

∙ 09/25/2017

Underwater Multi-Robot Convoying using Visual Tracking by Detection

We present a robust multi-robot convoying approach that relies on visual...

0 Florian Shkurti, et al. ∙

research

∙ 09/21/2017

Cost Adaptation for Robust Decentralized Swarm Behaviour

The multi-agent swarm system is a robust paradigm which can drive effici...

0 Peter Henderson, et al. ∙

research

∙ 09/19/2017

Deep Reinforcement Learning that Matters

In recent years, significant progress has been made in solving challengi...

1 Peter Henderson, et al. ∙

research

∙ 08/14/2017

Benchmark Environments for Multitask Learning in Continuous Domains

As demand drives systems to generalize to various domains and problems, ...

0 Peter Henderson, et al. ∙

research

∙ 12/17/2015

A Survey of Available Corpora for Building Data-Driven Dialogue Systems

During the past decade, several areas of speech and language understandi...

0 Iulian Vlad Serban, et al. ∙

Peter Henderson

Featured Co-authors