Peter Henderson

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Amazon ∙ Stanford University ∙ McGill University

With Little Power Comes Great Responsibility

Despite its importance to experimental design, statistical power (the pr...
10/13/2020 ∙ by Dallas Card, et al. ∙ 5 ∙ share

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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,...
07/21/2020 ∙ by Shagun Sodhani, et al. ∙ 44 ∙ share

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TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

We investigate whether Jacobi preconditioning, accounting for the bootst...
07/06/2020 ∙ by Joshua Romoff, et al. ∙ 0 ∙ share

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Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

With the recent wave of progress in artificial intelligence (AI) has com...
04/15/2020 ∙ by Miles Brundage, et al. ∙ 0 ∙ share

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

Accurate reporting of energy and carbon usage is essential for understan...
01/31/2020 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Separating value functions across time-scales

In many finite horizon episodic reinforcement learning (RL) settings, it...
02/05/2019 ∙ by Joshua Romoff, et al. ∙ 0 ∙ share

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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...
12/03/2018 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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An Introduction to Deep Reinforcement Learning

Deep reinforcement learning is the combination of reinforcement learning...
11/30/2018 ∙ by Vincent Francois-Lavet, et al. ∙ 28 ∙ share

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The RLLChatbot: a solution to the ConvAI Challenge

Current conversational systems can follow simple commands and answer bas...
11/07/2018 ∙ by Nicolas A. Gontier, et al. ∙ 0 ∙ share

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Adversarial Gain

Adversarial examples can be defined as inputs to a model which induce a ...
11/04/2018 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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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...
10/05/2018 ∙ by Peter Henderson, et al. ∙ 12 ∙ share

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Reward Estimation for Variance Reduction in Deep Reinforcement Learning

In reinforcement learning (RL), stochastic environments can make learnin...
05/09/2018 ∙ by Joshua Romoff, et al. ∙ 0 ∙ share

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Learning Robust Dialog Policies in Noisy Environments

Modern virtual personal assistants provide a convenient interface for co...
12/11/2017 ∙ by Maryam Fazel-Zarandi, et al. ∙ 0 ∙ share

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Ethical Challenges in Data-Driven Dialogue Systems

The use of dialogue systems as a medium for human-machine interaction is...
11/24/2017 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Underwater Multi-Robot Convoying using Visual Tracking by Detection

We present a robust multi-robot convoying approach that relies on visual...
09/25/2017 ∙ by Florian Shkurti, et al. ∙ 0 ∙ share

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Cost Adaptation for Robust Decentralized Swarm Behaviour

The multi-agent swarm system is a robust paradigm which can drive effici...
09/21/2017 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning that Matters

In recent years, significant progress has been made in solving challengi...
09/19/2017 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Benchmark Environments for Multitask Learning in Continuous Domains

As demand drives systems to generalize to various domains and problems, ...
08/14/2017 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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

During the past decade, several areas of speech and language understandi...
12/17/2015 ∙ by Iulian Vlad Serban, et al. ∙ 0 ∙ share

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Peter Henderson

Featured Co-authors

Yoshua Bengio

Joelle Pineau

Doina Precup

Emma Brunskill

Dan Jurafsky

Adrian Weller

Marc G. Bellemare

Junaed Sattar

Laurent Charlin

Yann Ollivier

David Meger

With Little Power Comes Great Responsibility

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

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

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

Separating value functions across time-scales

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

An Introduction to Deep Reinforcement Learning

The RLLChatbot: a solution to the ConvAI Challenge

Adversarial Gain

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

Reward Estimation for Variance Reduction in Deep Reinforcement Learning

Learning Robust Dialog Policies in Noisy Environments

Ethical Challenges in Data-Driven Dialogue Systems

Underwater Multi-Robot Convoying using Visual Tracking by Detection

Cost Adaptation for Robust Decentralized Swarm Behaviour

Deep Reinforcement Learning that Matters

Benchmark Environments for Multitask Learning in Continuous Domains

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