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2020 – today
- 2024
[c26]Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clément Farabet, Shane Legg:
Position: Levels of AGI for Operationalizing Progress on the Path to AGI. ICML 2024- [i51]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 abs/2404.10179 (2024) - 2023
- [c25]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: 1889-1903 - [c24]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 - [i50]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 abs/2305.16843 (2023) - [i49]Thomas McGrath, Matthew Rahtz, János Kramár, Vladimir Mikulik, Shane Legg:
The Hydra Effect: Emergent Self-repair in Language Model Computations. CoRR abs/2307.15771 (2023) - [i48]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 abs/2311.02462 (2023) - 2022
- [j7]Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega:
Your Policy Regularizer is Secretly an Adversary. Trans. Mach. Learn. Res. 2022 (2022) - [i47]Matthew Rahtz, Vikrant Varma, Ramana Kumar, Zachary Kenton, Shane Legg, Jan Leike:
Safe Deep RL in 3D Environments using Human Feedback. CoRR abs/2201.08102 (2022) - [i46]Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega:
Your Policy Regularizer is Secretly an Adversary. CoRR abs/2203.12592 (2022) - [i45]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 abs/2207.02098 (2022) - [i44]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 abs/2209.15618 (2022) - 2021
- [c23]Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg:
Agent Incentives: A Causal Perspective. AAAI 2021: 11487-11495 - [c22]Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike:
Quantifying Differences in Reward Functions. ICLR 2021 - [i43]Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg:
Agent Incentives: A Causal Perspective. CoRR abs/2102.01685 (2021) - [i42]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 abs/2103.03938 (2021) - [i41]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 abs/2110.10819 (2021) - [i40]Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega:
Model-Free Risk-Sensitive Reinforcement Learning. CoRR abs/2111.02907 (2021) - 2020
- [j6]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): 1-100 (2020) - [c21]Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike:
Learning Human Objectives by Evaluating Hypothetical Behavior. ICML 2020: 8020-8029 - [c20]Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg:
Pitfalls of Learning a Reward Function Online. IJCAI 2020: 1592-1600 - [c19]Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg:
Avoiding Side Effects By Considering Future Tasks. NeurIPS 2020 - [c18]Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega:
Meta-trained agents implement Bayes-optimal agents. NeurIPS 2020 - [i39]Ryan Carey, Eric D. Langlois, Tom Everitt, Shane Legg:
The Incentives that Shape Behaviour. CoRR abs/2001.07118 (2020) - [i38]Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg:
Pitfalls of learning a reward function online. CoRR abs/2004.13654 (2020) - [i37]Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike:
Quantifying Differences in Reward Functions. CoRR abs/2006.13900 (2020) - [i36]Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg:
Avoiding Side Effects By Considering Future Tasks. CoRR abs/2010.07877 (2020) - [i35]Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega:
Meta-trained agents implement Bayes-optimal agents. CoRR abs/2010.11223 (2020) - [i34]Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega:
Algorithms for Causal Reasoning in Probability Trees. CoRR abs/2010.12237 (2020) - [i33]Ramana Kumar, Jonathan Uesato, Richard Ngo, Tom Everitt, Victoria Krakovna, Shane Legg:
REALab: An Embedded Perspective on Tampering. CoRR abs/2011.08820 (2020) - [i32]Jonathan Uesato, Ramana Kumar, Victoria Krakovna, Tom Everitt, Richard Ngo, Shane Legg:
Avoiding Tampering Incentives in Deep RL via Decoupled Approval. CoRR abs/2011.08827 (2020)
2010 – 2019
- 2019
- [c17]Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg:
Modeling AGI Safety Frameworks with Causal Influence Diagrams. AISafety@IJCAI 2019 - [c16]Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg:
Penalizing Side Effects using Stepwise Relative Reachability. AISafety@IJCAI 2019 - [i31]Laurent Orseau, Tor Lattimore, Shane Legg:
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. CoRR abs/1901.02230 (2019) - [i30]Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg:
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings. CoRR abs/1902.09980 (2019) - [i29]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:
Meta-learning of Sequential Strategies. CoRR abs/1905.03030 (2019) - [i28]Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg:
Modeling AGI Safety Frameworks with Causal Influence Diagrams. CoRR abs/1906.08663 (2019) - [i27]Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike:
Learning Human Objectives by Evaluating Hypothetical Behavior. CoRR abs/1912.05652 (2019) - 2018
- [c15]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 - [c14]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: 1406-1415 - [c13]Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei:
Reward learning from human preferences and demonstrations in Atari. NeurIPS 2018: 8022-8034 - [i26]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:
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. CoRR abs/1801.08116 (2018) - [i25]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 abs/1802.01561 (2018) - [i24]Laurent Orseau, Simon McGregor McGill, Shane Legg:
Agents and Devices: A Relative Definition of Agency. CoRR abs/1805.12387 (2018) - [i23]Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg:
Measuring and avoiding side effects using relative reachability. CoRR abs/1806.01186 (2018) - [i22]Pedro A. Ortega, Shane Legg:
Modeling Friends and Foes. CoRR abs/1807.00196 (2018) - [i21]Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei:
Reward learning from human preferences and demonstrations in Atari. CoRR abs/1811.06521 (2018) - [i20]Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg:
Scalable agent alignment via reward modeling: a research direction. CoRR abs/1811.07871 (2018) - [i19]Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli:
Scaling shared model governance via model splitting. CoRR abs/1812.05979 (2018) - 2017
- [c12]Laurent Orseau, Tor Lattimore, Shane Legg:
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. ALT 2017: 372-399 - [c11]Tom Everitt, Victoria Krakovna, Laurent Orseau, Shane Legg:
Reinforcement Learning with a Corrupted Reward Channel. IJCAI 2017: 4705-4713 - [c10]Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei:
Deep Reinforcement Learning from Human Preferences. NIPS 2017: 4299-4307 - [i18]Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, Shane Legg:
Reinforcement Learning with a Corrupted Reward Channel. CoRR abs/1705.08417 (2017) - [i17]Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei:
Deep reinforcement learning from human preferences. CoRR abs/1706.03741 (2017) - [i16]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 abs/1706.10295 (2017) - [i15]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 abs/1711.05074 (2017) - [i14]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:
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR abs/1711.08378 (2017) - [i13]Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg:
AI Safety Gridworlds. CoRR abs/1711.09883 (2017) - 2016
- [i12]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 abs/1612.03801 (2016) - 2015
- [j5]Stuart 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): 3-4 (2015) - [j4]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): 529-533 (2015) - [i11]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 abs/1507.04296 (2015) - 2014
- [c9]Shane Legg:
From academia to industry: The story of Google DeepMind. ICCSW 2014: 1 - 2011
- [c8]Shane Legg, Joel Veness:
An Approximation of the Universal Intelligence Measure. Algorithmic Probability and Friends 2011: 236-249 - [i10]Shane Legg, Joel Veness:
An Approximation of the Universal Intelligence Measure. CoRR abs/1109.5951 (2011)
2000 – 2009
- 2008
- [i9]Marcus Hutter, Shane Legg:
Temporal Difference Updating without a Learning Rate. CoRR abs/0810.5631 (2008) - 2007
- [j3]Shane Legg, Marcus Hutter:
Universal Intelligence: A Definition of Machine Intelligence. Minds Mach. 17(4): 391-444 (2007) - [j2]Marcus Hutter, Shane Legg, Paul M. B. Vitányi:
Algorithmic probability. Scholarpedia 2(8): 2572 (2007) - [c7]Marcus Hutter, Shane Legg:
Temporal Difference Updating without a Learning Rate. NIPS 2007: 705-712 - [i8]Shane Legg, Marcus Hutter:
A Collection of Definitions of Intelligence. CoRR abs/0706.3639 (2007) - [i7]Shane Legg, Marcus Hutter:
Universal Intelligence: A Definition of Machine Intelligence. CoRR abs/0712.3329 (2007) - [i6]Shane Legg, Marcus Hutter:
Tests of Machine Intelligence. CoRR abs/0712.3825 (2007) - 2006
- [j1]Marcus Hutter, Shane Legg:
Fitness uniform optimization. IEEE Trans. Evol. Comput. 10(5): 568-589 (2006) - [c6]Shane Legg, Marcus Hutter:
A Collection of Definitions of Intelligence. AGI 2006: 17-24 - [c5]Shane Legg, Marcus Hutter:
Tests of Machine Intelligence. 50 Years of Artificial Intelligence 2006: 232-242 - [c4]Shane Legg:
Is There an Elegant Universal Theory of Prediction? ALT 2006: 274-287 - [i5]Shane Legg, Marcus Hutter:
A Formal Measure of Machine Intelligence. CoRR abs/cs/0605024 (2006) - [i4]Shane Legg:
Is there an Elegant Universal Theory of Prediction? CoRR abs/cs/0606070 (2006) - [i3]Marcus Hutter, Shane Legg:
Fitness Uniform Optimization. CoRR abs/cs/0610126 (2006) - 2005
- [c3]Shane Legg, Marcus Hutter:
Fitness uniform deletion: a simple way to preserve diversity. GECCO 2005: 1271-1278 - [c2]Shane Legg, Marcus Hutter:
A Universal Measure of Intelligence for Artificial Agents. IJCAI 2005: 1509-1510 - [i2]Shane Legg, Marcus Hutter:
Fitness Uniform Deletion: A Simple Way to Preserve Diversity. CoRR abs/cs/0504035 (2005) - 2004
- [c1]Shane Legg, Marcus Hutter, Akshat Kumar:
Tournament versus fitness uniform selection. IEEE Congress on Evolutionary Computation 2004: 2144-2151 - [i1]Shane Legg, Marcus Hutter, Akshat Kumar:
Tournament versus Fitness Uniform Selection. CoRR cs.LG/0403038 (2004) - 2000
- [p1]Cristian S. Calude, Helmut Jürgensen, Shane Legg:
Solving Problems with Finite Test Sets. Finite Versus Infinite 2000: 39-52
Coauthor Index
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