Tom Schaul - RankMe

110 papers A* 20A 8B 2Journal 50Unranked 27

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Agency Is Frame-Dependent. CoRR David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	A*	`conf`	AuPair: Golden Example Pairs for Code Repair. ICML Aditi Mavalankar, Hassan Mansoor, Zita Marinho, Mariia Samsikova, Tom Schaul
2025	J	`jnl`	AuPair: Golden Example Pairs for Code Repair. CoRR Aditi Mavalankar, Hassan Mansoor, Zita Marinho, Masha Samsikova, Tom Schaul
2025	J	`jnl`	DataRater: Meta-Learned Dataset Curation. CoRR Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa M. Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeffrey Dean, Hado van Hasselt, David Silver
2025	J	`jnl`	Plasticity as the Mirror of Empowerment. CoRR David Abel, Michael Bowling, André Barreto, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2024	J	`jnl`	Boundless Socratic Learning with Language Games. CoRR Tom Schaul
2024	J	`jnl`	Open-Endedness is Essential for Artificial Superhuman Intelligence. CoRR Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel
2024	A*	`conf`	Position: Open-Endedness is Essential for Artificial Superhuman Intelligence. ICML Edward Hughes, Michael D. Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel
2023	A	`conf`	Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. GECCO Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dalibard, Sebastian Flennerhag
2023	J	`jnl`	Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. CoRR Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dalibard, Sebastian Flennerhag
2023	—	`conf`	Discovering Evolution Strategies via Meta-Black-Box Optimization. GECCO Companion Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
2023	A*	`conf`	Discovering Evolution Strategies via Meta-Black-Box Optimization. ICLR Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
2023	A*	`conf`	Scaling Goal-based Exploration via Pruning Proto-goals. IJCAI Akhil Bagaria, Tom Schaul
2023	J	`jnl`	Scaling Goal-based Exploration via Pruning Proto-goals. CoRR Akhil Bagaria, Ray Jiang, Ramana Kumar, Tom Schaul
2023	J	`jnl`	Vision-Language Models as a Source of Rewards. CoRR Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, 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 Zhang
2022	J	`jnl`	AI for the Social Good (Dagstuhl Seminar 22091). Dagstuhl Reports Claudia Clopath, Ruben De Winne, Tom Schaul
2022	J	`jnl`	Discovering Evolution Strategies via Meta-Black-Box Optimization. CoRR Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dallibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
2022	A*	`conf`	Model-Value Inconsistency as a Signal for Epistemic Uncertainty. ICML Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero
2022	A*	`conf`	The Phenomenon of Policy Churn. NeurIPS Tom Schaul, André Barreto, John Quan, Georg Ostrovski
2022	J	`jnl`	The Phenomenon of Policy Churn. CoRR Tom Schaul, André Barreto, John Quan, Georg Ostrovski
2022	A*	`conf`	When should agents explore? ICLR Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana L. Borsa, Tom Schaul
2021	J	`jnl`	Model-Value Inconsistency as a Signal for Epistemic Uncertainty. CoRR Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero
2021	J	`jnl`	Return-based Scaling: Yet Another Normalisation Trick for Deep RL. CoRR Tom Schaul, Georg Ostrovski, Iurii Kemaev, Diana Borsa
2021	J	`jnl`	When should agents explore? CoRR Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana Borsa, Tom Schaul
2020	A	`conf`	Conditional Importance Sampling for Off-Policy Learning. AISTATS Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
2020	J	`jnl`	Policy Evaluation Networks. CoRR Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon
2019	J	`jnl`	AI for the Social Good (Dagstuhl Seminar 19082). Dagstuhl Reports Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, Tom Schaul
2019	J	`jnl`	Adapting Behaviour for Learning Progress. CoRR Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero
2019	J	`jnl`	Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511). Dagstuhl Reports Jialin Liu, Tom Schaul, Pieter Spronck, Julian Togelius
2019	J	`jnl`	Conditional Importance Sampling for Off-Policy Learning. CoRR Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
2019	J	`jnl`	Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nat. Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver
2019	J	`jnl`	Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods. CoRR Karel Lenc, Erich Elsen, Tom Schaul, Karen Simonyan
2019	J	`jnl`	Ray Interference: a Source of Plateaus in Deep Reinforcement Learning. CoRR Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu
2019	J	`jnl`	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
2019	—	`conf`	Universal Successor Features Approximators. ICLR (Poster) Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul
2018	A*	`conf`	Deep Q-learning From Demonstrations. AAAI 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
2018	J	`jnl`	Meta-Learning by the Baldwin Effect. CoRR Chrisantha Thomas Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu
2018	—	`conf`	Meta-learning by the Baldwin effect. GECCO (Companion) Chrisantha Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu
2018	—	`conf`	Meta-learning by the baldwin effect. GECCO (Companion) Chrisantha Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu
2018	A*	`conf`	Rainbow: Combining Improvements in Deep Reinforcement Learning. AAAI Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
2018	J	`jnl`	The Barbados 2018 List of Open Issues in Continual Learning. CoRR Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup
2018	A*	`conf`	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
2018	J	`jnl`	Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul
2018	J	`jnl`	Universal Successor Features Approximators. CoRR Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
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	A*	`conf`	FeUdal Networks for Hierarchical Reinforcement Learning. ICML Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu
2017	J	`jnl`	FeUdal Networks for Hierarchical Reinforcement Learning. CoRR Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu
2017	J	`jnl`	Learning from Demonstrations for Real World Reinforcement Learning. CoRR Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys
2017	—	`conf`	Natural Value Approximators: Learning when to Trust Past Estimates. NIPS Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul
2017	J	`jnl`	Rainbow: Combining Improvements in Deep Reinforcement Learning. CoRR Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Daniel Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
2017	—	`conf`	Reinforcement Learning with Unsupervised Auxiliary Tasks. ICLR Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu
2017	J	`jnl`	StarCraft II: A New Challenge for Reinforcement Learning. CoRR Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing
2017	—	`conf`	Successor Features for Transfer in Reinforcement Learning. NIPS André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt
2017	A*	`conf`	The Predictron: End-To-End Learning and Planning. ICML David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris
2016	—	`conf`	Analyzing the robustness of general video game playing agents. CIG Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas
2016	A*	`conf`	Dueling Network Architectures for Deep Reinforcement Learning. ICML Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
2016	A*	`conf`	General Video Game AI: Competition, Challenges and Opportunities. AAAI Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas
2016	—	`conf`	Learning to learn by gradient descent by gradient descent. NIPS Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
2016	J	`jnl`	Learning to learn by gradient descent by gradient descent. CoRR Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
2016	A*	`conf`	Prioritized Experience Replay. ICLR (Poster) Tom Schaul, John Quan, Ioannis Antonoglou, David Silver
2016	J	`jnl`	Reinforcement Learning with Unsupervised Auxiliary Tasks. CoRR Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu
2016	J	`jnl`	Successor Features for Transfer in Reinforcement Learning. CoRR André Barreto, Rémi Munos, Tom Schaul, David Silver
2016	J	`jnl`	The 2014 General Video Game Playing Competition. IEEE Trans. Comput. Intell. AI Games Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas, Adrien Couëtoux, Jerry Lee, Chong-U Lim, Tommy Thompson
2016	J	`jnl`	The Predictron: End-To-End Learning and Planning. CoRR David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris
2016	—	`conf`	Unifying Count-Based Exploration and Intrinsic Motivation. NIPS Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos
2016	J	`jnl`	Unifying Count-Based Exploration and Intrinsic Motivation. CoRR Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos
2015	A*	`conf`	Universal Value Function Approximators. ICML Tom Schaul, Daniel Horgan, Karol Gregor, David Silver
2014	J	`jnl`	An Extensible Description Language for Video Games. IEEE Trans. Comput. Intell. AI Games Tom Schaul
2014	J	`jnl`	Natural evolution strategies. J. Mach. Learn. Res. Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber
2014	—	`conf`	Unit Tests for Stochastic Optimization. ICLR Tom Schaul, Ioannis Antonoglou, David Silver
2013	—	`conf`	A linear time natural evolution strategy for non-separable functions. GECCO (Companion) Yi Sun, Tom Schaul, Faustino J. Gomez, Jürgen Schmidhuber
2013	—	`conf`	A video game description language for model-based or interactive learning. CIG Tom Schaul
2013	A*	`conf`	Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients ICLR (Poster) Tom Schaul, Yann LeCun
2013	A*	`conf`	Better Generalization with Forecasts. IJCAI Tom Schaul, Mark B. Ring
2013	—	`ch.`	General Video Game Playing. Artificial and Computational Intelligence in Games John Levine, Clare Bates Congdon, Marc Ebner, Graham Kendall, Simon M. Lucas, Risto Miikkulainen, Tom Schaul, Tommy Thompson
2013	—	`conf`	No more pesky learning rates. ICML (3) Tom Schaul, Sixin Zhang, Yann LeCun
2013	—	`ch.`	Towards a Video Game Description Language. Artificial and Computational Intelligence in Games Marc Ebner, John Levine, Simon M. Lucas, Tom Schaul, Tommy Thompson, Julian Togelius
2012	—	`conf`	Benchmarking exponential natural evolution strategies on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) Tom Schaul
2012	—	`conf`	Benchmarking natural evolution strategies with adaptation sampling on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) Tom Schaul
2012	—	`conf`	Benchmarking separable natural evolution strategies on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) Tom Schaul
2012	—	`conf`	Comparing natural evolution strategies to BIPOP-CMA-ES on noiseless and noisy black-box optimization testbeds. GECCO (Companion) Tom Schaul
2012	J	`jnl`	Efficient Natural Evolution Strategies CoRR Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
2012	—	`conf`	Investigating the impact of adaptation sampling in natural evolution strategies on black-box optimization testbeds. GECCO (Companion) Tom Schaul
2012	A	`conf`	Natural evolution strategies converge on sphere functions. GECCO Tom Schaul
2012	J	`jnl`	No More Pesky Learning Rates CoRR Tom Schaul, Sixin Zhang, Yann LeCun
2012	—	`conf`	The organization of behavior into temporal and spatial neighborhoods. ICDL-EPIROB Mark B. Ring, Tom Schaul
2011	J	`jnl`	A Linear Time Natural Evolution Strategy for Non-Separable Functions CoRR Yi Sun, Faustino J. Gomez, Tom Schaul, Jürgen Schmidhuber
2011	—	`conf`	Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. AGI Tom Schaul, Leo Pape, Tobias Glasmachers, Vincent Graziano, Jürgen Schmidhuber
2011	B	`conf`	Curiosity-driven optimization. IEEE Congress on Evolutionary Computation Tom Schaul, Yi Sun, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber
2011	A	`conf`	High dimensions and heavy tails for natural evolution strategies. GECCO Tom Schaul, Tobias Glasmachers, Jürgen Schmidhuber
2011	J	`jnl`	Measuring Intelligence through Games CoRR Tom Schaul, Julian Togelius, Jürgen Schmidhuber
2011	J	`jnl`	Natural Evolution Strategies CoRR Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber
2011	A*	`conf`	Q-Error as a Selection Mechanism in Modular Reinforcement-Learning Systems. IJCAI Mark B. Ring, Tom Schaul
2011	—	`—`	Studies in Continuous Black-box Optimization. Tom Schaul
2011	—	`conf`	The two-dimensional organization of behavior. ICDL-EPIROB Mark B. Ring, Tom Schaul, Jürgen Schmidhuber
2010	—	`conf`	A Natural Evolution Strategy for Multi-objective Optimization. PPSN (1) Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber
2010	J	`jnl`	Exploring parameter space in reinforcement learning. Paladyn J. Behav. Robotics Thomas Rückstieß, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, Jürgen Schmidhuber
2010	A	`conf`	Exponential natural evolution strategies. GECCO Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber
2010	J	`jnl`	Metalearning. Scholarpedia Tom Schaul, Jürgen Schmidhuber
2010	—	`conf`	Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients. ICANN (2) Mandy Grüttner, Frank Sehnke, Tom Schaul, Jürgen Schmidhuber
2010	J	`jnl`	PyBrain. J. Mach. Learn. Res. Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber
2009	A	`conf`	Efficient natural evolution strategies. GECCO Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
2009	J	`jnl`	Ontogenetic and Phylogenetic Reinforcement Learning. Künstliche Intell. Tom Schaul, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber, Christian Igel, Julian Togelius
2009	—	`conf`	Scalable Neural Networks for Board Games. ICANN (1) Tom Schaul, Jürgen Schmidhuber
2009	A*	`conf`	Stochastic search using the natural gradient. ICML Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
2008	—	`conf`	A scalable neural network architecture for board games. CIG Tom Schaul, Jürgen Schmidhuber
2008	A	`conf`	Countering Poisonous Inputs with Memetic Neuroevolution. PPSN Julian Togelius, Tom Schaul, Jürgen Schmidhuber, Faustino J. Gomez
2008	—	`conf`	Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. ICANN (1) Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber
2008	A	`conf`	Fitness Expectation Maximization. PPSN Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber
2008	B	`conf`	Natural Evolution Strategies. IEEE Congress on Evolutionary Computation Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber