Theophane Weber

Publication Activity (10 Years)

Years Active: 2008-2023
Publications (10 Years): 44

Top Topics

Gradient Estimation

Credit Assignment

Monte Carlo Tree Search

Interaction Networks

Top Venues

Publications

Andreea Deac, Theophane Weber, George Papamakarios
Equivariant MuZero. Trans. Mach. Learn. Res. 2023 (2023)
Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Anirudh Goyal, Mélanie Rey, Theophane Weber, Matthew M. Botvinick, Michael Curtis Mozer, Danilo Jimenez Rezende
Learning to Induce Causal Structure. ICLR (2023)
Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos
Quantile Credit Assignment. ICML (2023)
Nan Rosemary Ke, Sara-Jane Dunn, Jörg Bornschein, Silvia Chiappa, Mélanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane X. Wang, Theophane Weber, David G. T. Barrett, Matthew M. Botvinick, Anirudh Goyal, Michael Mozer, Danilo J. Rezende
DiscoGen: Learning to Discover Gene Regulatory Networks. CoRR (2023)
Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Theophane Weber, Jessica B. Hamrick
Investigating the Role of Model-Based Learning in Exploration and Transfer. ICML (2023)
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
Retrieval-Augmented Reinforcement Learning. ICML (2022)
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell
Retrieval-Augmented Reinforcement Learning. CoRR (2022)
Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap
Large-Scale Retrieval for Reinforcement Learning. NeurIPS (2022)
Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende
Learning to Induce Causal Structure. CoRR (2022)
Ankesh Anand, Jacob C. Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Theophane Weber, Jessica B. Hamrick
Procedural generalization by planning with self-supervised world models. ICLR (2022)
David Raposo, Samuel Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt M. Botvinick, Hado van Hasselt, H. Francis Song
Synthetic Returns for Long-Term Credit Assignment. CoRR (2021)
Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S. Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Rémi Munos
Counterfactual Credit Assignment in Model-Free Reinforcement Learning. ICML (2021)
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
Muesli: Combining Improvements in Policy Optimization. ICML (2021)
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Velickovic, Theophane Weber
On the role of planning in model-based deep reinforcement learning. ICLR (2021)
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
Muesli: Combining Improvements in Policy Optimization. CoRR (2021)
Danilo J. Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing
Causally Correct Partial Models for Reinforcement Learning. CoRR (2020)
Péter Karkus, Mehdi Mirza, Arthur Guez, Andrew Jaegle, Timothy P. Lillicrap, Lars Buesing, Nicolas Heess, Theophane Weber
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban. CoRR (2020)
Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, Peter W. Battaglia
Combining Q-Learning and Search with Amortized Value Estimates. ICLR (2020)
Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess
Value-driven Hindsight Modelling. NeurIPS (2020)
Lars Buesing, Nicolas Heess, Theophane Weber
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. AISTATS (2020)
Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning. CoRR (2020)
Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed
A case for new neural network smoothness constraints. CoRR (2020)
Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed
A case for new neural network smoothness constraints. ICBINB@NeurIPS (2020)
Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, Peter W. Battaglia
Combining Q-Learning and Search with Amortized Value Estimates. CoRR (2019)
Lars Buesing, Nicolas Heess, Theophane Weber
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. CoRR (2019)
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap
An Investigation of Model-Free Planning. ICML (2019)
Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. ICLR (Poster) (2019)
Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber
Temporal Difference Variational Auto-Encoder. ICLR (2019)
John F. J. Mellor, Eunbyung Park, Yaroslav Ganin, Igor Babuschkin, Tejas Kulkarni, Dan Rosenbaum, Andy Ballard, Theophane Weber, Oriol Vinyals, S. M. Ali Eslami
Unsupervised Doodling and Painting with Improved SPIRAL. CoRR (2019)
Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber
Single-Agent Policy Tree Search With Guarantees. NeurIPS (2018)
Lars Buesing, Theophane Weber, Yori Zwols, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. CoRR (2018)
Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap
Relational recurrent neural networks. CoRR (2018)
Lars Buesing, Theophane Weber, Sébastien Racanière, S. M. Ali Eslami, Danilo Jimenez Rezende, David P. Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, Daan Wierstra
Learning and Querying Fast Generative Models for Reinforcement Learning. CoRR (2018)
Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
Learning to Search with MCTSnets. ICML (2018)
Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap
Relational recurrent neural networks. NeurIPS (2018)
Sébastien Racanière, Theophane Weber, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, Demis Hassabis, David Silver, Daan Wierstra
Imagination-Augmented Agents for Deep Reinforcement Learning. NIPS (2017)
Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, David Silver, Daan Wierstra
Imagination-Augmented Agents for Deep Reinforcement Learning. CoRR (2017)
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter W. Battaglia, Razvan Pascanu, Andrea Tacchetti
Visual Interaction Networks: Learning a Physics Simulator from Video. NIPS (2017)
Nicholas Watters, Andrea Tacchetti, Theophane Weber, Razvan Pascanu, Peter W. Battaglia, Daniel Zoran
Visual Interaction Networks. CoRR (2017)
Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sébastien Racanière, David P. Reichert, Theophane Weber, Daan Wierstra, Peter W. Battaglia
Learning model-based planning from scratch. 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)
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. NIPS (2016)
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, Geoffrey E. Hinton
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. CoRR (2016)
Hari Chauhan, Marvin Onabajo, Vladimir Kvartenko, Robin Coxe, Theophane Weber
An Optimization Platform for Digital Predistortion of Power Amplifiers. IEEE Des. Test 33 (2) (2016)
John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
Gradient Estimation Using Stochastic Computation Graphs. CoRR (2015)
John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
Gradient Estimation Using Stochastic Computation Graphs. NIPS (2015)
David Gamarnik, David A. Goldberg, Theophane Weber
Correlation Decay in Random Decision Networks. Math. Oper. Res. 39 (2) (2014)
David Wingate, Theophane Weber
Automated Variational Inference in Probabilistic Programming CoRR (2013)
Shawn Hershey, Jeffrey Bernstein, Bill Bradley, Andrew Schweitzer, Noah Stein, Theophane Weber, Benjamin Vigoda
Accelerating Inference: towards a full Language, Compiler and Hardware stack CoRR (2012)
Justin Dauwels, Theophane Weber, François B. Vialatte, Toshimitsu Musha, Andrzej Cichocki
> 2 Point Processes. Neural Comput. 24 (2) (2012)
Benjamin Vigoda, David Reynolds, Jeffrey Bernstein, Theophane Weber, Bill Bradley
Low power logic for statistical inference. ISLPED (2010)
David Gamarnik, David A. Goldberg, Theophane Weber
PTAS for Maximum Weight Independent Set Problem with Random Weights in Bounded Degree Graphs. SODA (2010)
Justin Dauwels, François B. Vialatte, Theophane Weber, Toshimitsu Musha, Andrzej Cichocki
Quantifying Statistical Interdependence by Message Passing on Graphs - Part II: Multidimensional Point Processes. Neural Comput. 21 (8) (2009)
David Gamarnik, David A. Goldberg, Theophane Weber
Correlation Decay in Random Decision Networks CoRR (2009)
Justin Dauwels, François B. Vialatte, Theophane Weber, Andrzej Cichocki
Quantifying Statistical Interdependence by Message Passing on Graphs - Part I: One-Dimensional Point Processes. Neural Comput. 21 (8) (2009)
Justin Dauwels, François B. Vialatte, Theophane Weber, Andrzej Cichocki
Analyzing brain signals by combinatorial optimization. Allerton (2008)
Justin Dauwels, François B. Vialatte, Theophane Weber, Andrzej Cichocki
An Exemplar-Based Statistical Model for the Dynamics of Neural Synchrony. ICONIP (1) (2008)
Justin Dauwels, François B. Vialatte, Theophane Weber, Andrzej Cichocki
On Similarity Measures for Spike Trains. ICONIP (1) (2008)