Shane Legg

According to our database, Shane Legg authored at least 61 papers between 2000 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Levels of AGI: Operationalizing Progress on the Path to AGI.

Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clément Farabet, Shane Legg

CoRR, 2023

The Hydra Effect: Emergent Self-repair in Language Model Computations.

Thomas McGrath, Matthew Rahtz, János Kramár, Vladimir Mikulik, Shane Legg

CoRR, 2023

Neural Networks and the Chomsky Hierarchy.

Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Chris Cundy, Marcus Hutter, Shane Legg, Joel Veness, Pedro A. Ortega

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Randomized Positional Encodings Boost Length Generalization of Transformers.

Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023

2022

Your Policy Regularizer is Secretly an Adversary.

Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega

Trans. Mach. Learn. Res., 2022

Beyond Bayes-optimality: meta-learning what you know you don't know.

Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Tim Genewein, Elliot Catt, Kevin Li, Anian Ruoss, Chris Cundy, Joel Veness, Jane X. Wang, Marcus Hutter, Christopher Summerfield, Shane Legg, Pedro A. Ortega

CoRR, 2022

Neural Networks and the Chomsky Hierarchy.

Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, Pedro A. Ortega

CoRR, 2022

Safe Deep RL in 3D Environments using Human Feedback.

Matthew Rahtz, Vikrant Varma, Ramana Kumar, Zachary Kenton, Shane Legg, Jan Leike

CoRR, 2022

2021

Model-Free Risk-Sensitive Reinforcement Learning.

Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega

CoRR, 2021

Shaking the foundations: delusions in sequence models for interaction and control.

Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli, Jonas Degrave, Bilal Piot, Julien Pérolat, Tom Everitt, Corentin Tallec, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, Shane Legg

CoRR, 2021

Causal Analysis of Agent Behavior for AI Safety.

Grégoire Delétang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega

CoRR, 2021

Quantifying Differences in Reward Functions.

Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike

Proceedings of the 9th International Conference on Learning Representations, 2021

Agent Incentives: A Causal Perspective.

Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Special Issue "On Defining Artificial Intelligence" - Commentaries and Author's Response.

Dagmar Monett, Colin W. P. Lewis, Kristinn R. Thórisson, Joscha Bach, Gianluca Baldassarre, Giovanni Granato, Istvan S. N. Berkeley, François Chollet, Matthew Crosby, Henry Shevlin, John F. Sowa, John E. Laird, Shane Legg, Peter Lindes, Tomás Mikolov, William J. Rapaport, Raúl Rojas, Marek Rosa, Peter Stone, Richard S. Sutton, Roman V. Yampolskiy, Pei Wang, Roger C. Schank, Aaron Sloman, Alan F. T. Winfield

J. Artif. Gen. Intell., 2020

Avoiding Tampering Incentives in Deep RL via Decoupled Approval.

Jonathan Uesato, Ramana Kumar, Victoria Krakovna, Tom Everitt, Richard Ngo, Shane Legg

CoRR, 2020

REALab: An Embedded Perspective on Tampering.

Ramana Kumar, Jonathan Uesato, Richard Ngo, Tom Everitt, Victoria Krakovna, Shane Legg

CoRR, 2020

Algorithms for Causal Reasoning in Probability Trees.

Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega

CoRR, 2020

The Incentives that Shape Behaviour.

Ryan Carey, Eric D. Langlois, Tom Everitt, Shane Legg

CoRR, 2020

Meta-trained agents implement Bayes-optimal agents.

Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Avoiding Side Effects By Considering Future Tasks.

Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Pitfalls of Learning a Reward Function Online.

Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Learning Human Objectives by Evaluating Hypothetical Behavior.

Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Meta-learning of Sequential Strategies.

Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg

CoRR, 2019

Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings.

Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg

CoRR, 2019

Penalizing Side Effects using Stepwise Relative Reachability.

Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg

Proceedings of the Workshop on Artificial Intelligence Safety 2019 co-located with the 28th International Joint Conference on Artificial Intelligence, 2019

Modeling AGI Safety Frameworks with Causal Influence Diagrams.

Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg

Proceedings of the Workshop on Artificial Intelligence Safety 2019 co-located with the 28th International Joint Conference on Artificial Intelligence, 2019

2018

Scaling shared model governance via model splitting.

Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli

CoRR, 2018

Scalable agent alignment via reward modeling: a research direction.

Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg

CoRR, 2018

Modeling Friends and Foes.

Pedro A. Ortega, Shane Legg

CoRR, 2018

Measuring and avoiding side effects using relative reachability.

Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg

CoRR, 2018

Agents and Devices: A Relative Definition of Agency.

Laurent Orseau, Simon McGregor McGill, Shane Legg

CoRR, 2018

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents.

Joel Z. Leibo, Cyprien de Masson d'Autume, Daniel Zoran, David Amos, Charles Beattie, Keith Anderson, Antonio García Castañeda, Manuel Sanchez, Simon Green, Audrunas Gruslys, Shane Legg, Demis Hassabis, Matthew M. Botvinick

CoRR, 2018

Reward learning from human preferences and demonstrations in Atari.

Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.

Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu

Proceedings of the 35th International Conference on Machine Learning, 2018

Noisy Networks For Exploration.

Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

AI Safety Gridworlds.

Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg

CoRR, 2017

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017.

Matthew M. Botvinick, David G. T. Barrett, Peter W. Battaglia, Nando de Freitas, Dharshan Kumaran, Joel Z. Leibo, Tim Lillicrap, Joseph Modayil, S. Mohamed, Neil C. Rabinowitz, Danilo Jimenez Rezende, Adam Santoro, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis

CoRR, 2017

Symmetric Decomposition of Asymmetric Games.

Karl Tuyls, Julien Pérolat, Marc Lanctot, Georg Ostrovski, Rahul Savani, Joel Z. Leibo, Toby Ord, Thore Graepel, Shane Legg

CoRR, 2017

Noisy Networks for Exploration.

Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

CoRR, 2017

Reinforcement Learning with a Corrupted Reward Channel.

Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, Shane Legg

CoRR, 2017

Deep Reinforcement Learning from Human Preferences.

Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Reinforcement Learning with a Corrupted Reward Channel.

Tom Everitt, Victoria Krakovna, Laurent Orseau, Shane Legg

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Soft-Bayes: Prod for Mixtures of Experts with Log-Loss.

Laurent Orseau, Tor Lattimore, Shane Legg

Proceedings of the International Conference on Algorithmic Learning Theory, 2017

2016

DeepMind Lab.

Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, Stig Petersen

CoRR, 2016

2015

Human-level control through deep reinforcement learning.

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis

Nat., 2015

Massively Parallel Methods for Deep Reinforcement Learning.

Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, Shane Legg, Volodymyr Mnih, Koray Kavukcuoglu, David Silver

CoRR, 2015

Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter.

Stuart Russell, Tom Dietterich, Eric Horvitz, Bart Selman, Francesca Rossi, Demis Hassabis, Shane Legg, Mustafa Suleyman, Dileep George, D. Scott Phoenix

AI Mag., 2015

2014

From academia to industry: The story of Google DeepMind.

Shane Legg

Proceedings of the 2014 Imperial College Computing Student Workshop, 2014

2011

An Approximation of the Universal Intelligence Measure.

Shane Legg, Joel Veness

Proceedings of the Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, 2011

2007

Algorithmic probability.

Marcus Hutter, Shane Legg, Paul M. B. Vitányi

Scholarpedia, 2007

Universal Intelligence: A Definition of Machine Intelligence.

Shane Legg, Marcus Hutter

Minds Mach., 2007

Temporal Difference Updating without a Learning Rate.

Marcus Hutter, Shane Legg

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

Fitness uniform optimization.

Marcus Hutter, Shane Legg

IEEE Trans. Evol. Comput., 2006

A Formal Measure of Machine Intelligence

Shane Legg, Marcus Hutter

CoRR, 2006

Is There an Elegant Universal Theory of Prediction?

Shane Legg

Proceedings of the Algorithmic Learning Theory, 17th International Conference, 2006

Tests of Machine Intelligence.

Shane Legg, Marcus Hutter

Proceedings of the 50 Years of Artificial Intelligence, 2006

A Collection of Definitions of Intelligence.

Shane Legg, Marcus Hutter

Proceedings of the Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 2006

2005

A Universal Measure of Intelligence for Artificial Agents.

Shane Legg, Marcus Hutter

Proceedings of the IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, July 30, 2005

Fitness uniform deletion: a simple way to preserve diversity.

Shane Legg, Marcus Hutter

Proceedings of the Genetic and Evolutionary Computation Conference, 2005

2004

Tournament versus fitness uniform selection.

Shane Legg, Marcus Hutter, Akshat Kumar

Proceedings of the IEEE Congress on Evolutionary Computation, 2004

2000

Solving Problems with Finite Test Sets.

Cristian S. Calude, Helmut Jürgensen, Shane Legg

Proceedings of the Finite Versus Infinite, 2000