Shane Legg

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 58

Top Topics

Scalable Distributed

Influence Diagrams

Reinforcement Learning

Top Venues

Publications

SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young
Scaling Instructable Agents Across Many Simulated Worlds. CoRR (2024)
Thomas McGrath, Matthew Rahtz, János Kramár, Vladimir Mikulik, Shane Legg
The Hydra Effect: Emergent Self-repair in Language Model Computations. CoRR (2023)
Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness
Randomized Positional Encodings Boost Length Generalization of Transformers. CoRR (2023)
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
Neural Networks and the Chomsky Hierarchy. ICLR (2023)
Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness
Randomized Positional Encodings Boost Length Generalization of Transformers. ACL (2) (2023)
Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clément Farabet, Shane Legg
Levels of AGI: Operationalizing Progress on the Path to AGI. CoRR (2023)
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
Beyond Bayes-optimality: meta-learning what you know you don't know. CoRR (2022)
Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Gregoire Detetang, Markus Kunesch, Shane Legg, Pedro A. Ortega
Your Policy Regularizer is Secretly an Adversary. Trans. Mach. Learn. Res. 2022 (2022)
Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega
Your Policy Regularizer is Secretly an Adversary. CoRR (2022)
Matthew Rahtz, Vikrant Varma, Ramana Kumar, Zachary Kenton, Shane Legg, Jan Leike
Safe Deep RL in 3D Environments using Human Feedback. CoRR (2022)
Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, Pedro A. Ortega
Neural Networks and the Chomsky Hierarchy. CoRR (2022)
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
Shaking the foundations: delusions in sequence models for interaction and control. CoRR (2021)
Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike
Quantifying Differences in Reward Functions. ICLR (2021)
Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega
Model-Free Risk-Sensitive Reinforcement Learning. CoRR (2021)
Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg
Agent Incentives: A Causal Perspective. CoRR (2021)
Grégoire Delétang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega
Causal Analysis of Agent Behavior for AI Safety. CoRR (2021)
Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg
Agent Incentives: A Causal Perspective. AAAI (2021)
Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike
Learning Human Objectives by Evaluating Hypothetical Behavior. ICML (2020)
Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg
Avoiding Side Effects By Considering Future Tasks. CoRR (2020)
Ramana Kumar, Jonathan Uesato, Richard Ngo, Tom Everitt, Victoria Krakovna, Shane Legg
REALab: An Embedded Perspective on Tampering. CoRR (2020)
Ryan Carey, Eric D. Langlois, Tom Everitt, Shane Legg
The Incentives that Shape Behaviour. CoRR (2020)
Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega
Algorithms for Causal Reasoning in Probability Trees. CoRR (2020)
Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
Meta-trained agents implement Bayes-optimal agents. CoRR (2020)
Jonathan Uesato, Ramana Kumar, Victoria Krakovna, Tom Everitt, Richard Ngo, Shane Legg
Avoiding Tampering Incentives in Deep RL via Decoupled Approval. CoRR (2020)
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
Special Issue "On Defining Artificial Intelligence" - Commentaries and Author's Response. J. Artif. Gen. Intell. 11 (2) (2020)
Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg
Avoiding Side Effects By Considering Future Tasks. NeurIPS (2020)
Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg
Pitfalls of Learning a Reward Function Online. IJCAI (2020)
Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
Meta-trained agents implement Bayes-optimal agents. NeurIPS (2020)
Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg
Pitfalls of learning a reward function online. CoRR (2020)
Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike
Quantifying Differences in Reward Functions. CoRR (2020)
Laurent Orseau, Tor Lattimore, Shane Legg
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. CoRR (2019)
Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike
Learning Human Objectives by Evaluating Hypothetical Behavior. CoRR (2019)
Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg
Modeling AGI Safety Frameworks with Causal Influence Diagrams. CoRR (2019)
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 Botvinick, Shane Legg
Meta-learning of Sequential Strategies. CoRR (2019)
Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg
Modeling AGI Safety Frameworks with Causal Influence Diagrams. AISafety@IJCAI (2019)
Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg
Penalizing Side Effects using Stepwise Relative Reachability. AISafety@IJCAI (2019)
Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings. CoRR (2019)
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei
Reward learning from human preferences and demonstrations in Atari. CoRR (2018)
Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli
Scaling shared model governance via model splitting. CoRR (2018)
Pedro A. Ortega, Shane Legg
Modeling Friends and Foes. CoRR (2018)
Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg
Measuring and avoiding side effects using relative reachability. CoRR (2018)
Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. ICML (2018)
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg
Scalable agent alignment via reward modeling: a research direction. CoRR (2018)
Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. CoRR (2018)
Laurent Orseau, Simon McGregor McGill, Shane Legg
Agents and Devices: A Relative Definition of Agency. CoRR (2018)
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 Botvinick
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. CoRR (2018)
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei
Reward learning from human preferences and demonstrations in Atari. NeurIPS (2018)
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
Noisy Networks For Exploration. ICLR (Poster) (2018)
Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei
Deep Reinforcement Learning from Human Preferences. NIPS (2017)
Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg
AI Safety Gridworlds. CoRR (2017)
Laurent Orseau, Tor Lattimore, Shane Legg
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. ALT (2017)
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
Noisy Networks for Exploration. CoRR (2017)
Matthew 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
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR (2017)
Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, Shane Legg
Reinforcement Learning with a Corrupted Reward Channel. CoRR (2017)
Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei
Deep reinforcement learning from human preferences. CoRR (2017)
Karl Tuyls, Julien Pérolat, Marc Lanctot, Georg Ostrovski, Rahul Savani, Joel Z. Leibo, Toby Ord, Thore Graepel, Shane Legg
Symmetric Decomposition of Asymmetric Games. CoRR (2017)
Tom Everitt, Victoria Krakovna, Laurent Orseau, Shane Legg
Reinforcement Learning with a Corrupted Reward Channel. IJCAI (2017)
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
DeepMind Lab. CoRR (2016)
Stuart J. Russell, Tom Dietterich, Eric Horvitz, Bart Selman, Francesca Rossi, Demis Hassabis, Shane Legg, Mustafa Suleyman, Dileep George, D. Scott Phoenix
Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter. AI Mag. 36 (4) (2015)
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
Human-level control through deep reinforcement learning. Nat. 518 (7540) (2015)
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
Massively Parallel Methods for Deep Reinforcement Learning. CoRR (2015)
Shane Legg
From academia to industry: The story of Google DeepMind. ICCSW (2014)
Shane Legg, Joel Veness
An Approximation of the Universal Intelligence Measure CoRR (2011)
Shane Legg, Joel Veness
An Approximation of the Universal Intelligence Measure. Algorithmic Probability and Friends (2011)
Marcus Hutter, Shane Legg
Temporal Difference Updating without a Learning Rate CoRR (2008)
Marcus Hutter, Shane Legg
Temporal Difference Updating without a Learning Rate. NIPS (2007)
Marcus Hutter, Shane Legg, Paul M. B. Vitányi
Algorithmic probability. Scholarpedia 2 (8) (2007)
Shane Legg, Marcus Hutter
Universal Intelligence: A Definition of Machine Intelligence CoRR (2007)
Shane Legg, Marcus Hutter
A Collection of Definitions of Intelligence CoRR (2007)
Shane Legg, Marcus Hutter
Universal Intelligence: A Definition of Machine Intelligence. Minds Mach. 17 (4) (2007)
Shane Legg, Marcus Hutter
Tests of Machine Intelligence CoRR (2007)
Shane Legg, Marcus Hutter
A Collection of Definitions of Intelligence. AGI (2006)
Marcus Hutter, Shane Legg
Fitness uniform optimization. IEEE Trans. Evol. Comput. 10 (5) (2006)
Shane Legg
Is There an Elegant Universal Theory of Prediction? ALT (2006)
Marcus Hutter, Shane Legg
Fitness Uniform Optimization CoRR (2006)
Shane Legg, Marcus Hutter
A Formal Measure of Machine Intelligence CoRR (2006)
Shane Legg, Marcus Hutter
Tests of Machine Intelligence. 50 Years of Artificial Intelligence (2006)
Shane Legg
Is there an Elegant Universal Theory of Prediction? CoRR (2006)
Shane Legg, Marcus Hutter
A Universal Measure of Intelligence for Artificial Agents. IJCAI (2005)
Shane Legg, Marcus Hutter
Fitness uniform deletion: a simple way to preserve diversity. GECCO (2005)
Shane Legg, Marcus Hutter
Fitness Uniform Deletion: A Simple Way to Preserve Diversity CoRR (2005)
Shane Legg, Marcus Hutter, Akshat Kumar
Tournament versus fitness uniform selection. IEEE Congress on Evolutionary Computation (2004)
Shane Legg, Marcus Hutter, Akshat Kumar
Tournament versus Fitness Uniform Selection CoRR (2004)