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Theophane Weber
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Journal Articles
- 2023
- [j7]Andreea Deac, Theophane Weber, George Papamakarios:
Equivariant MuZero. Trans. Mach. Learn. Res. 2023 (2023) - 2016
- [j6]Hari Chauhan, Marvin Onabajo, Vladimir Kvartenko, Robin Coxe, Theophane Weber:
An Optimization Platform for Digital Predistortion of Power Amplifiers. IEEE Des. Test 33(2): 49-58 (2016) - 2014
- [j5]David Gamarnik, David A. Goldberg, Theophane Weber:
Correlation Decay in Random Decision Networks. Math. Oper. Res. 39(2): 229-261 (2014) - 2012
- [j4]Justin Dauwels, Theophane Weber, François B. Vialatte
, Toshimitsu Musha, Andrzej Cichocki
:
Quantifying Statistical Interdependence, Part III: N > 2 Point Processes. Neural Comput. 24(2): 408-454 (2012) - 2010
- [j3]Jérémie Gallien
, Théophane Weber:
To Wave or Not to Wave? Order Release Policies for Warehouses with an Automated Sorter. Manuf. Serv. Oper. Manag. 12(4): 642-662 (2010) - 2009
- [j2]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): 2152-2202 (2009) - [j1]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): 2203-2268 (2009)
Conference and Workshop Papers
- 2023
- [c30]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 - [c29]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: 24517-24531 - [c28]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: 35368-35383 - 2022
- [c27]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 - [c26]Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell:
Retrieval-Augmented Reinforcement Learning. ICML 2022: 7740-7765 - [c25]Peter Conway Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap:
Large-Scale Retrieval for Reinforcement Learning. NeurIPS 2022 - 2021
- [c24]Jessica B. Hamrick, Abram L. Friesen, Feryal M. P. 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 - [c23]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: 4214-4226 - [c22]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: 7654-7664 - 2020
- [c21]Lars Buesing, Nicolas Heess, Theophane Weber:
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. AISTATS 2020: 624-634 - [c20]Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed:
A case for new neural network smoothness constraints. ICBINB@NeurIPS 2020: 21-32 - [c19]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 - [c18]Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess:
Value-driven Hindsight Modelling. NeurIPS 2020 - 2019
- [c17]Théophane Weber, Nicolas Heess, Lars Buesing, David Silver:
Credit Assignment Techniques in Stochastic Computation Graphs. AISTATS 2019: 2650-2660 - [c16]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 - [c15]Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber:
Temporal Difference Variational Auto-Encoder. ICLR 2019 - [c14]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: 2464-2473 - 2018
- [c13]Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver:
Learning to Search with MCTSnets. ICML 2018: 1817-1826 - [c12]Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber:
Single-Agent Policy Tree Search With Guarantees. NeurIPS 2018: 3205-3215 - [c11]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: 7310-7321 - 2017
- [c10]Nicholas Watters, Daniel Zoran, Theophane Weber, Peter W. Battaglia, Razvan Pascanu, Andrea Tacchetti:
Visual Interaction Networks: Learning a Physics Simulator from Video. NIPS 2017: 4539-4547 - [c9]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: 5690-5701 - 2016
- [c8]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: 3225-3233 - 2015
- [c7]John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel:
Gradient Estimation Using Stochastic Computation Graphs. NIPS 2015: 3528-3536 - 2010
- [c6]Benjamin Vigoda, David Reynolds, Jeffrey Bernstein, Theophane Weber, Bill Bradley:
Low power logic for statistical inference. ISLPED 2010: 349-354 - [c5]David Gamarnik, David A. Goldberg, Theophane Weber:
PTAS for Maximum Weight Independent Set Problem with Random Weights in Bounded Degree Graphs. SODA 2010: 268-278 - 2008
- [c4]Justin Dauwels, François B. Vialatte
, Theophane Weber, Andrzej Cichocki
:
Analyzing brain signals by combinatorial optimization. Allerton 2008: 1381-1388 - [c3]Daniela Pucci de Farias, Théophane Weber:
Choosing the cost vector of the linear programming approach to approximate dynamic programming. CDC 2008: 67-72 - [c2]Justin Dauwels, François B. Vialatte
, Theophane Weber, Andrzej Cichocki
:
On Similarity Measures for Spike Trains. ICONIP (1) 2008: 177-185 - [c1]Justin Dauwels, François B. Vialatte
, Theophane Weber, Andrzej Cichocki
:
An Exemplar-Based Statistical Model for the Dynamics of Neural Synchrony. ICONIP (1) 2008: 318-326
Informal and Other Publications
- 2023
- [i38]Pol Moreno, Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Rosália G. Schneider, Björn Winckler, Larisa Markeeva, Théophane Weber, Danilo J. Rezende:
Laser: Latent Set Representations for 3D Generative Modeling. CoRR abs/2301.05747 (2023) - [i37]Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Théophane Weber, Jessica B. Hamrick:
Investigating the role of model-based learning in exploration and transfer. CoRR abs/2302.04009 (2023) - [i36]Andreea Deac, Théophane Weber, George Papamakarios:
Equivariant MuZero. CoRR abs/2302.04798 (2023) - [i35]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 abs/2304.05823 (2023) - 2022
- [i34]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 abs/2202.08417 (2022) - [i33]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 abs/2204.04875 (2022) - [i32]Peter Conway Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy P. Lillicrap:
Large-Scale Retrieval for Reinforcement Learning. CoRR abs/2206.05314 (2022) - 2021
- [i31]Pol Moreno, Edward Hughes, Kevin R. McKee, Bernardo Ávila Pires, Théophane Weber:
Neural Recursive Belief States in Multi-Agent Reinforcement Learning. CoRR abs/2102.02274 (2021) - [i30]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 abs/2102.12425 (2021) - [i29]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 abs/2104.06159 (2021) - [i28]Ankesh Anand, Jacob C. Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Théophane Weber, Jessica B. Hamrick:
Procedural Generalization by Planning with Self-Supervised World Models. CoRR abs/2111.01587 (2021) - 2020
- [i27]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 abs/2002.02836 (2020) - [i26]Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess:
Value-driven Hindsight Modelling. CoRR abs/2002.08329 (2020) - [i25]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 abs/2004.11410 (2020) - [i24]Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt, Arthur Guez, Saran Tunyasuvunakool, Alistair Muldal, Théophane Weber, Péter Karkus, Sébastien Racanière, Lars Buesing, Timothy P. Lillicrap, Nicolas Heess:
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning. CoRR abs/2009.05524 (2020) - [i23]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 abs/2010.01298 (2020) - [i22]Jessica B. Hamrick, Abram L. Friesen, Feryal M. P. Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Velickovic, Théophane Weber:
On the role of planning in model-based deep reinforcement learning. CoRR abs/2011.04021 (2020) - [i21]Thomas Mesnard, Théophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Tom Stepleton, Nicolas Heess, Arthur Guez, Marcus Hutter, Lars Buesing, Rémi Munos:
Counterfactual Credit Assignment in Model-Free Reinforcement Learning. CoRR abs/2011.09464 (2020) - [i20]Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed:
A case for new neural network smoothness constraints. CoRR abs/2012.07969 (2020) - 2019
- [i19]Théophane Weber, Nicolas Heess, Lars Buesing, David Silver:
Credit Assignment Techniques in Stochastic Computation Graphs. CoRR abs/1901.01761 (2019) - [i18]Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap:
An investigation of model-free planning. CoRR abs/1901.03559 (2019) - [i17]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 abs/1910.01007 (2019) - [i16]Lars Buesing, Nicolas Heess, Theophane Weber:
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. CoRR abs/1910.06862 (2019) - [i15]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 abs/1912.02807 (2019) - 2018
- [i14]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 abs/1802.03006 (2018) - [i13]Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver:
Learning to Search with MCTSnets. CoRR abs/1802.04697 (2018) - [i12]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 abs/1806.01822 (2018) - [i11]Lars Buesing, Theophane Weber, Yori Zwols, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess:
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. CoRR abs/1811.06272 (2018) - [i10]Laurent Orseau, Levi H. S. Lelis, Tor Lattimore, Théophane Weber:
Single-Agent Policy Tree Search With Guarantees. CoRR abs/1811.10928 (2018) - 2017
- [i9]Nicholas Watters, Andrea Tacchetti, Theophane Weber, Razvan Pascanu, Peter W. Battaglia, Daniel Zoran:
Visual Interaction Networks. CoRR abs/1706.01433 (2017) - [i8]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 abs/1707.06170 (2017) - [i7]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 abs/1707.06203 (2017) - [i6]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) - 2016
- [i5]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 abs/1603.08575 (2016) - 2015
- [i4]John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel:
Gradient Estimation Using Stochastic Computation Graphs. CoRR abs/1506.05254 (2015) - 2013
- [i3]David Wingate, Theophane Weber:
Automated Variational Inference in Probabilistic Programming. CoRR abs/1301.1299 (2013) - 2012
- [i2]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 abs/1212.2991 (2012) - 2009
- [i1]David Gamarnik, David A. Goldberg, Theophane Weber:
Correlation Decay in Random Decision Networks. CoRR abs/0912.0338 (2009)
Coauthor Index
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