Schaul, Tom

36 publications

ICML 2025 AuPair: Golden Example Pairs for Code Repair Aditi Mavalankar, Hassan Mansoor, Zita Marinho, Mariia Samsikova, Tom Schaul

NeurIPS 2025 DataRater: Meta-Learned Dataset Curation Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeff Dean, Hado van Hasselt, David Silver

NeurIPS 2025 Plasticity as the Mirror of Empowerment David Abel, Michael Bowling, Andre Barreto, Will Dabney, Shi Dong, Steven Stenberg Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh

NeurIPSW 2024 Boundless Socratic Learning with Language Games Tom Schaul

ICML 2024 Position: Open-Endedness Is Essential for Artificial Superhuman Intelligence Edward Hughes, Michael D Dennis, Jack Parker-Holder, Feryal Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel

ICLR 2023 Discovering Evolution Strategies via Meta-Black-Box Optimization Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag

IJCAI 2023 Scaling Goal-Based Exploration via Pruning Proto-Goals Akhil Bagaria, Tom Schaul

NeurIPSW 2023 Vision-Language Models as a Source of Rewards Kate Baumli, Satinder Singh, Feryal Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei M Zhang

ICML 2022 Model-Value Inconsistency as a Signal for Epistemic Uncertainty Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram Friesen, Feryal Behbahani, Tom Schaul, Andre Barreto, Simon Osindero

ICLRW 2022 Model-Value Inconsistency as a Signal for Epistemic Uncertainty Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana L Borsa, Abram L. Friesen, Feryal Behbahani, Tom Schaul, Andre Barreto, Simon Osindero

NeurIPS 2022 The Phenomenon of Policy Churn Tom Schaul, Andre Barreto, John Quan, Georg Ostrovski

ICLR 2022 When Should Agents Explore? Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana L Borsa, Tom Schaul

AISTATS 2020 Conditional Importance Sampling for Off-Policy Learning Mark Rowland, Anna Harutyunyan, Hado Hasselt, Diana Borsa, Tom Schaul, Remi Munos, Will Dabney

ICLR 2019 Universal Successor Features Approximators Diana Borsa, Andre Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Remi Munos, David Silver, Tom Schaul

AAAI 2018 Deep Q-Learning from Demonstrations Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

AAAI 2018 Rainbow: Combining Improvements in Deep Reinforcement Learning Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver

ICML 2018 Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, Remi Munos

ICML 2017 FeUdal Networks for Hierarchical Reinforcement Learning Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu

NeurIPS 2017 Natural Value Approximators: Learning When to Trust past Estimates Zhongwen Xu, Joseph Modayil, Hado P van Hasselt, Andre Barreto, David Silver, Tom Schaul

ICLR 2017 Reinforcement Learning with Unsupervised Auxiliary Tasks Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu

NeurIPS 2017 Successor Features for Transfer in Reinforcement Learning Andre Barreto, Will Dabney, Remi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, David Silver

ICML 2017 The Predictron: End-to-End Learning and Planning David Silver, Hado Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris

ICML 2016 Dueling Network Architectures for Deep Reinforcement Learning Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, Nando Freitas

AAAI 2016 General Video Game AI: Competition, Challenges and Opportunities Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas

NeurIPS 2016 Learning to Learn by Gradient Descent by Gradient Descent Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas

ICLR 2016 Prioritized Experience Replay Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

NeurIPS 2016 Unifying Count-Based Exploration and Intrinsic Motivation Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos

ICML 2015 Universal Value Function Approximators Tom Schaul, Daniel Horgan, Karol Gregor, David Silver

JMLR 2014 Natural Evolution Strategies Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber

ICLR 2014 Unit Tests for Stochastic Optimization Tom Schaul, Ioannis Antonoglou, David Silver

ICLR 2013 Adaptive Learning Rates and Parallelization for Stochastic, Sparse, Non-Smooth Gradients Tom Schaul, Yann LeCun

IJCAI 2013 Better Generalization with Forecasts Tom Schaul, Mark B. Ring

ICML 2013 No More Pesky Learning Rates Tom Schaul, Sixin Zhang, Yann LeCun

IJCAI 2011 Q-Error as a Selection Mechanism in Modular Reinforcement-Learning Systems Mark B. Ring, Tom Schaul

JMLR 2010 PyBrain Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber

ICML 2009 Stochastic Search Using the Natural Gradient Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber