Shane Legg - RankMe

88 papers A* 12A 1B 3Journal 61Unranked 10

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An Approach to Technical AGI Safety and Security. CoRR Rohin Shah, Alex Irpan, Alexander Matt Turner, Anna Wang, Arthur Conmy, David Lindner, Jonah Brown-Cohen, Lewis Ho, Neel Nanda, Raluca Ada Popa, Rishub Jain, Rory Greig, Samuel Albanie, Scott Emmons, Sebastian Farquhar, Sébastien Krier, Senthooran Rajamanoharan, Sophie Bridgers, Tobi Ijitoye, Tom Everitt, Victoria Krakovna, Vikrant Varma, Vladimir Mikulik, Zachary Kenton, Dave Orr, Shane Legg, Noah D. Goodman, Allan Dafoe, Four Flynn, Anca D. Dragan
2025	J	`jnl`	Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety. CoRR Tomek Korbak, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen, Alan Cooney, Allan Dafoe, Anca D. Dragan, Scott Emmons, Owain Evans, David Farhi, Ryan Greenblatt, Dan Hendrycks, Marius Hobbhahn, Evan Hubinger, Geoffrey Irving, Erik Jenner, Daniel Kokotajlo, Victoria Krakovna, Shane Legg, David Lindner, David Luan, Aleksander Madry, Julian Michael, Neel Nanda, Dave Orr, Jakub Pachocki, Ethan Perez, Mary Phuong, Fabien Roger, Joshua Saxe, Buck Shlegeris, Martín Soto, Eric Steinberger, Jasmine Wang, Wojciech Zaremba, Bowen Baker, Rohin Shah, Vladimir Mikulik
2025	J	`jnl`	Incentives for responsiveness, instrumental control and impact. Artif. Intell. Ryan Carey, Eric D. Langlois, Chris van Merwijk, Shane Legg, Tom Everitt
2024	A*	`conf`	Position: Levels of AGI for Operationalizing Progress on the Path to AGI. ICML Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clément Farabet, Shane Legg
2024	J	`jnl`	Scaling Instructable Agents Across Many Simulated Worlds. CoRR 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
2023	J	`jnl`	Levels of AGI: Operationalizing Progress on the Path to AGI. CoRR Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clément Farabet, Shane Legg
2023	A*	`conf`	Neural Networks and the Chomsky Hierarchy. ICLR 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
2023	—	`conf`	Randomized Positional Encodings Boost Length Generalization of Transformers. ACL (2) Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness
2023	J	`jnl`	Randomized Positional Encodings Boost Length Generalization of Transformers. CoRR Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness
2023	J	`jnl`	The Hydra Effect: Emergent Self-repair in Language Model Computations. CoRR Thomas McGrath, Matthew Rahtz, János Kramár, Vladimir Mikulik, Shane Legg
2022	J	`jnl`	Beyond Bayes-optimality: meta-learning what you know you don't know. CoRR 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
2022	J	`jnl`	Neural Networks and the Chomsky Hierarchy. CoRR Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, Pedro A. Ortega
2022	J	`jnl`	Safe Deep RL in 3D Environments using Human Feedback. CoRR Matthew Rahtz, Vikrant Varma, Ramana Kumar, Zachary Kenton, Shane Legg, Jan Leike
2022	J	`jnl`	Your Policy Regularizer is Secretly an Adversary. CoRR Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega
2022	J	`jnl`	Your Policy Regularizer is Secretly an Adversary. Trans. Mach. Learn. Res. Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega
2021	A*	`conf`	Agent Incentives: A Causal Perspective. AAAI Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg
2021	J	`jnl`	Agent Incentives: A Causal Perspective. CoRR Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg
2021	J	`jnl`	Causal Analysis of Agent Behavior for AI Safety. CoRR Grégoire Delétang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega
2021	J	`jnl`	Model-Free Risk-Sensitive Reinforcement Learning. CoRR Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega
2021	A*	`conf`	Quantifying Differences in Reward Functions. ICLR Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike
2021	J	`jnl`	Shaking the foundations: delusions in sequence models for interaction and control. CoRR 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
2020	J	`jnl`	Algorithms for Causal Reasoning in Probability Trees. CoRR Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega
2020	A*	`conf`	Avoiding Side Effects By Considering Future Tasks. NeurIPS Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg
2020	J	`jnl`	Avoiding Side Effects By Considering Future Tasks. CoRR Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg
2020	J	`jnl`	Avoiding Tampering Incentives in Deep RL via Decoupled Approval. CoRR Jonathan Uesato, Ramana Kumar, Victoria Krakovna, Tom Everitt, Richard Ngo, Shane Legg
2020	A*	`conf`	Learning Human Objectives by Evaluating Hypothetical Behavior. ICML Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike
2020	A*	`conf`	Meta-trained agents implement Bayes-optimal agents. NeurIPS Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
2020	J	`jnl`	Meta-trained agents implement Bayes-optimal agents. CoRR Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
2020	A*	`conf`	Pitfalls of Learning a Reward Function Online. IJCAI Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg
2020	J	`jnl`	Pitfalls of learning a reward function online. CoRR Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg
2020	J	`jnl`	Quantifying Differences in Reward Functions. CoRR Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike
2020	J	`jnl`	REALab: An Embedded Perspective on Tampering. CoRR Ramana Kumar, Jonathan Uesato, Richard Ngo, Tom Everitt, Victoria Krakovna, Shane Legg
2020	J	`jnl`	Special Issue "On Defining Artificial Intelligence" - Commentaries and Author's Response. J. Artif. Gen. Intell. 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
2020	J	`jnl`	The Incentives that Shape Behaviour. CoRR Ryan Carey, Eric D. Langlois, Tom Everitt, Shane Legg
2019	J	`jnl`	Learning Human Objectives by Evaluating Hypothetical Behavior. CoRR Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike
2019	J	`jnl`	Meta-learning of Sequential Strategies. CoRR 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
2019	—	`conf`	Modeling AGI Safety Frameworks with Causal Influence Diagrams. AISafety@IJCAI Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg
2019	J	`jnl`	Modeling AGI Safety Frameworks with Causal Influence Diagrams. CoRR Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg
2019	—	`conf`	Penalizing Side Effects using Stepwise Relative Reachability. AISafety@IJCAI Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg
2019	J	`jnl`	Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. CoRR Laurent Orseau, Tor Lattimore, Shane Legg
2019	J	`jnl`	Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings. CoRR Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg
2018	J	`jnl`	Agents and Devices: A Relative Definition of Agency. CoRR Laurent Orseau, Simon McGregor McGill, Shane Legg
2018	A*	`conf`	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. ICML Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
2018	J	`jnl`	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. CoRR Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
2018	J	`jnl`	Measuring and avoiding side effects using relative reachability. CoRR Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg
2018	J	`jnl`	Modeling Friends and Foes. CoRR Pedro A. Ortega, Shane Legg
2018	—	`conf`	Noisy Networks For Exploration. ICLR (Poster) 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
2018	J	`jnl`	Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. CoRR 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
2018	A*	`conf`	Reward learning from human preferences and demonstrations in Atari. NeurIPS Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei
2018	J	`jnl`	Reward learning from human preferences and demonstrations in Atari. CoRR Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei
2018	J	`jnl`	Scalable agent alignment via reward modeling: a research direction. CoRR Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg
2018	J	`jnl`	Scaling shared model governance via model splitting. CoRR Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli
2017	J	`jnl`	AI Safety Gridworlds. CoRR Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg
2017	J	`jnl`	Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR 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
2017	—	`conf`	Deep Reinforcement Learning from Human Preferences. NIPS Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei
2017	J	`jnl`	Deep reinforcement learning from human preferences. CoRR Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei
2017	J	`jnl`	Noisy Networks for Exploration. CoRR 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
2017	A*	`conf`	Reinforcement Learning with a Corrupted Reward Channel. IJCAI Tom Everitt, Victoria Krakovna, Laurent Orseau, Shane Legg
2017	J	`jnl`	Reinforcement Learning with a Corrupted Reward Channel. CoRR Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, Shane Legg
2017	B	`conf`	Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. ALT Laurent Orseau, Tor Lattimore, Shane Legg
2017	J	`jnl`	Symmetric Decomposition of Asymmetric Games. CoRR Karl Tuyls, Julien Pérolat, Marc Lanctot, Georg Ostrovski, Rahul Savani, Joel Z. Leibo, Toby Ord, Thore Graepel, Shane Legg
2016	J	`jnl`	DeepMind Lab. CoRR 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
2015	J	`jnl`	Human-level control through deep reinforcement learning. Nat. 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
2015	J	`jnl`	Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter. AI Mag. Stuart Russell, Tom Dietterich, Eric Horvitz, Bart Selman, Francesca Rossi, Demis Hassabis, Shane Legg, Mustafa Suleyman, Dileep George, D. Scott Phoenix
2015	J	`jnl`	Massively Parallel Methods for Deep Reinforcement Learning. CoRR 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
2014	—	`conf`	From academia to industry: The story of Google DeepMind. ICCSW Shane Legg
2011	J	`jnl`	An Approximation of the Universal Intelligence Measure CoRR Shane Legg, Joel Veness
2011	—	`conf`	An Approximation of the Universal Intelligence Measure. Algorithmic Probability and Friends Shane Legg, Joel Veness
2008	J	`jnl`	Temporal Difference Updating without a Learning Rate CoRR Marcus Hutter, Shane Legg
2007	J	`jnl`	A Collection of Definitions of Intelligence CoRR Shane Legg, Marcus Hutter
2007	J	`jnl`	Algorithmic probability. Scholarpedia Marcus Hutter, Shane Legg, Paul M. B. Vitányi
2007	—	`conf`	Temporal Difference Updating without a Learning Rate. NIPS Marcus Hutter, Shane Legg
2007	J	`jnl`	Tests of Machine Intelligence CoRR Shane Legg, Marcus Hutter
2007	J	`jnl`	Universal Intelligence: A Definition of Machine Intelligence CoRR Shane Legg, Marcus Hutter
2007	J	`jnl`	Universal Intelligence: A Definition of Machine Intelligence. Minds Mach. Shane Legg, Marcus Hutter
2006	—	`conf`	A Collection of Definitions of Intelligence. AGI Shane Legg, Marcus Hutter
2006	J	`jnl`	A Formal Measure of Machine Intelligence CoRR Shane Legg, Marcus Hutter
2006	J	`jnl`	Fitness Uniform Optimization CoRR Marcus Hutter, Shane Legg
2006	J	`jnl`	Fitness uniform optimization. IEEE Trans. Evol. Comput. Marcus Hutter, Shane Legg
2006	B	`conf`	Is There an Elegant Universal Theory of Prediction? ALT Shane Legg
2006	J	`jnl`	Is there an Elegant Universal Theory of Prediction? CoRR Shane Legg
2006	—	`conf`	Tests of Machine Intelligence. 50 Years of Artificial Intelligence Shane Legg, Marcus Hutter
2005	A*	`conf`	A Universal Measure of Intelligence for Artificial Agents. IJCAI Shane Legg, Marcus Hutter
2005	J	`jnl`	Fitness Uniform Deletion: A Simple Way to Preserve Diversity CoRR Shane Legg, Marcus Hutter
2005	A	`conf`	Fitness uniform deletion: a simple way to preserve diversity. GECCO Shane Legg, Marcus Hutter
2004	J	`jnl`	Tournament versus Fitness Uniform Selection CoRR Shane Legg, Marcus Hutter, Akshat Kumar
2004	B	`conf`	Tournament versus fitness uniform selection. IEEE Congress on Evolutionary Computation Shane Legg, Marcus Hutter, Akshat Kumar
2000	—	`ch.`	Solving Problems with Finite Test Sets. Finite Versus Infinite Cristian S. Calude, Helmut Jürgensen, Shane Legg