Owain Evans

Alexander Tamas Research Scientist in Artificial Intelligence, University of Oxford

I'm a research scientist working on AI Safety and Reinforcement Learning at the Future of Humanity Institute (directed by Nick Bostrom). My PhD is from MIT, where I worked on cognitive science, AI, and philosophy.

My name is pronounced "O-wine".

CV (pdf) | Email | Google Scholar | LinkedIn | Facebook

Research

Sensory Optimization: Neural Networks as a Model for Understanding and Creating Art
Evans, O. (2019)
Preprint
(PDF version)

Generalizing from a few environments in safety-critical reinforcement learning
Kenton Z., Filos A., Evans O., Gal Y. (2019)
ICLR 2019 (Safe ML Workshop)

Machine Learning Projects for Iterated Distillation and Amplification
Evans O., Saunders W., Stuhlmüller A. (2019)
Technical Report

Predicting Human Deliberative Judgments with Machine Learning
Evans O., Stuhlmüller A., Cundy C., Carey R., Kenton, Z., McGrath T., Schreiber A. (2018)
Technical Report

Active Reinforcement Learning with Monte-Carlo Tree Search
Schulze S., Evans O. (2018)
Arxiv Pre-print

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage M., Avin S., Clark J., et al. (2018)
Arxiv Pre-print.

Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
Saunders S., Sastry G., Stuhlmüller A., Evans O. (2017)
AAMAS 2018
(Blogpost, Atari Videos, Slides)

When Will AI Exceed Human Performance? Evidence from AI Experts.
Grace K., Salvatier J., Zhang B., Dafoe A., Evans O. (2017)
Journal of AI Research (JAIR) 2018.
(Covered by BBC News, New Scientist, Newsweek, and more)

Model Mis-specification and Inverse Reinforcement Learning.
(Essay co-authored with Jacob Steinhardt, 2017).

Agentmodels.org: Modeling Agents with Probabilistic Programs.
Evans O., Stuhlmüller A., Salvatier J., Filan D. (2017)
Online Book and Open-source Library

Agent-Agnostic Human-in-the-Loop Reinforcement Learning.
Abel D., Salvatier J., Stuhlmüller A., Evans O. (2016)
NIPS Workshop

Active Reinforcement Learning: Observing Rewards at a Cost.
Krueger D., Leike J, Salvatier J., Evans O. (2016)
NIPS Workshop

Learning the Preferences of Ignorant, Inconsistent Agents.
Evans O., Stuhlmüller A., Goodman N. (2016)
AAAI

Learning the Preferences of Bounded Agents.
Evans O., Stuhlmüller A., Goodman N. (2015)
NIPS Workshop

Learning Structured Preferences.
Evans O., Bergen L., Tenenbaum J. (2012)
Cogsci

Help or hinder: Bayesian models of social goal inference.
Ullman T., Baker C., Macindoe O., Evans O., Goodman N., & Tenenbaum J. (2010)
NIPS

Bayesian Computational Models for Inferring Preferences (2015)
MIT Dissertation

Talks

Predicting Slow Judgment
(Slides for talk at "Aligning AI" workshop at NIPS 2017 in Long Beach.)

Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
(Slides for talks at Cambridge Centre for the Future of Intelligence and Google Deepmind)

Automated Corporations and AI Risk
(Informal talk at Oxford University)

Agent-agnostic Human-in-the-loop Reinforcement Learning
(Slides for talks at U. Toronto and Deepmind)

Learning the Preferences of Ignorant, Inconsistent Agents
(Slides for oral presentation at AAAI 2016)

Learning Human Preferences
(Short talk at MIT)

Recent Collaborators

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Andreas Stuhlmüller 		description here
John Salvatier 		description here
Katja Grace
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Jan Leike 		description here
David Abel 		description here
David Krueger
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Allan Dafoe 		description here
Vlad Firoiu 		description here
William Saunders
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Baobao Zhang 		description here
Girish Sastry

Adapted from Matei Zaharia and Andreas Viklund.