Analyzing brain signals by combinatorial optimization. - Justin Dauwels; François B. Vialatte; Theophane Weber; Andrzej Cichocki (Allerton, 2008)

Choosing the cost vector of the linear programming approach to approximate dynamic programming. - Daniela Pucci de Farias; Théophane Weber (CDC, 2008)

On Similarity Measures for Spike Trains. - Justin Dauwels; François B. Vialatte; Theophane Weber; Andrzej Cichocki (ICONIP (1), 2008)

An Exemplar-Based Statistical Model for the Dynamics of Neural Synchrony. - Justin Dauwels; François B. Vialatte; Theophane Weber; Andrzej Cichocki (ICONIP (1), 2008)

Quantifying Statistical Interdependence by Message Passing on Graphs - Part I: One-Dimensional Point Processes. - Justin Dauwels; François B. Vialatte; Theophane Weber; Andrzej Cichocki (Neural Comput., 2009)

Quantifying Statistical Interdependence by Message Passing on Graphs - Part II: Multidimensional Point Processes. - Justin Dauwels; François B. Vialatte; Theophane Weber; Toshimitsu Musha; Andrzej Cichocki (Neural Comput., 2009)

Low power logic for statistical inference. - Benjamin Vigoda; David Reynolds; Jeffrey Bernstein; Theophane Weber; Bill Bradley (ISLPED, 2010)

PTAS for Maximum Weight Independent Set Problem with Random Weights in Bounded Degree Graphs. - David Gamarnik; David A. Goldberg 001; Theophane Weber (SODA, 2010)

To Wave or Not to Wave? Order Release Policies for Warehouses with an Automated Sorter. - Jérémie Gallien; Théophane Weber (Manuf. Serv. Oper. Manag., 2010)

Accelerating Inference: towards a full Language, Compiler and Hardware stack - Shawn Hershey; Jeffrey Bernstein; Bill Bradley; Andrew Schweitzer; Noah Stein; Theophane Weber; Benjamin Vigoda (CoRR, 2012)

Quantifying Statistical Interdependence, Part III: N > 2 Point Processes. - Justin Dauwels; Theophane Weber; François B. Vialatte; Toshimitsu Musha; Andrzej Cichocki (Neural Comput., 2012)

Automated Variational Inference in Probabilistic Programming - David Wingate; Theophane Weber (CoRR, 2013)

Correlation Decay in Random Decision Networks. - David Gamarnik; David A. Goldberg 001; Theophane Weber (Math. Oper. Res., 2014)

Gradient Estimation Using Stochastic Computation Graphs. - John Schulman; Nicolas Heess; Theophane Weber; Pieter Abbeel (NIPS, 2015)

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. - S. M. Ali Eslami; Nicolas Heess; Theophane Weber; Yuval Tassa; David Szepesvari; Koray Kavukcuoglu; Geoffrey E. Hinton (NIPS, 2016)

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. - S. M. Ali Eslami; Nicolas Heess; Theophane Weber; Yuval Tassa; Koray Kavukcuoglu; Geoffrey E. Hinton (CoRR, 2016)

An Optimization Platform for Digital Predistortion of Power Amplifiers. - Hari Chauhan; Marvin Onabajo; Vladimir Kvartenko; Robin Coxe; Theophane Weber (IEEE Des. Test, 2016)

Imagination-Augmented Agents for Deep Reinforcement Learning. - Sébastien Racanière; Theophane Weber; David P. Reichert; Lars Buesing; Arthur Guez; Danilo Jimenez Rezende; Adrià Puigdomènech Badia; Oriol Vinyals; Nicolas Heess; Yujia Li 001; Razvan Pascanu; Peter W. Battaglia; et al. (NIPS, 2017)

Visual Interaction Networks: Learning a Physics Simulator from Video. - Nicholas Watters; Daniel Zoran; Theophane Weber; Peter W. Battaglia; Razvan Pascanu; Andrea Tacchetti (NIPS, 2017)

Learning model-based planning from scratch. - Razvan Pascanu; Yujia Li 001; Oriol Vinyals; Nicolas Heess; Lars Buesing; Sébastien Racanière; David P. Reichert; Theophane Weber; Daan Wierstra; Peter W. Battaglia (CoRR, 2017)

Visual Interaction Networks. - Nicholas Watters; Andrea Tacchetti; Theophane Weber; Razvan Pascanu; Peter W. Battaglia; Daniel Zoran (CoRR, 2017)

Imagination-Augmented Agents for Deep Reinforcement Learning. - Theophane Weber; Sébastien Racanière; David P. Reichert; Lars Buesing; Arthur Guez; Danilo Jimenez Rezende; Adrià Puigdomènech Badia; Oriol Vinyals; Nicolas Heess; Yujia Li 001; Razvan Pascanu; Peter W. Battaglia; et al. (CoRR, 2017)

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. - 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; et al. (CoRR, 2017)

Learning to Search with MCTSnets. - Arthur Guez; Theophane Weber; Ioannis Antonoglou; Karen Simonyan; Oriol Vinyals; Daan Wierstra; Rémi Munos; David Silver (ICML, 2018)

Single-Agent Policy Tree Search With Guarantees. - Laurent Orseau; Levi Lelis; Tor Lattimore; Theophane Weber (NeurIPS, 2018)

Relational recurrent neural networks. - Adam Santoro; Ryan Faulkner 001; David Raposo; Jack W. Rae; Mike Chrzanowski; Theophane Weber; Daan Wierstra; Oriol Vinyals; Razvan Pascanu; Timothy P. Lillicrap (NeurIPS, 2018)

Learning and Querying Fast Generative Models for Reinforcement Learning. - 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 (CoRR, 2018)

Credit Assignment Techniques in Stochastic Computation Graphs. - Théophane Weber; Nicolas Heess; Lars Buesing; David Silver (AISTATS, 2019)

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. - Lars Buesing; Theophane Weber; Yori Zwols; Nicolas Heess; Sébastien Racanière; Arthur Guez; Jean-Baptiste Lespiau (ICLR (Poster), 2019)

Temporal Difference Variational Auto-Encoder. - Karol Gregor; George Papamakarios; Frederic Besse; Lars Buesing; Theophane Weber (ICLR, 2019)

An Investigation of Model-Free Planning. - Arthur Guez; Mehdi Mirza; Karol Gregor; Rishabh Kabra; Sébastien Racanière; Theophane Weber; David Raposo; Adam Santoro; Laurent Orseau; Tom Eccles; Greg Wayne; David Silver; et al. (ICML, 2019)

Unsupervised Doodling and Painting with Improved SPIRAL. - John F. J. Mellor; Eunbyung Park; Yaroslav Ganin; Igor Babuschkin; Tejas Kulkarni; Dan Rosenbaum; Andy Ballard; Theophane Weber; Oriol Vinyals; S. M. Ali Eslami (CoRR, 2019)

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. - Lars Buesing; Nicolas Heess; Theophane Weber (AISTATS, 2020)

A case for new neural network smoothness constraints. - Mihaela Rosca; Theophane Weber; Arthur Gretton; Shakir Mohamed (ICBINB@NeurIPS, 2020)

Combining Q-Learning and Search with Amortized Value Estimates. - Jessica B. Hamrick; Victor Bapst; Alvaro Sanchez-Gonzalez; Tobias Pfaff; Theophane Weber; Lars Buesing; Peter W. Battaglia (ICLR, 2020)

Value-driven Hindsight Modelling. - Arthur Guez; Fabio Viola; Theophane Weber; Lars Buesing; Steven Kapturowski; Doina Precup; David Silver; Nicolas Heess (NeurIPS, 2020)

Causally Correct Partial Models for Reinforcement Learning. - Danilo J. Rezende; Ivo Danihelka; George Papamakarios; Nan Rosemary Ke; Ray Jiang; Theophane Weber; Karol Gregor; Hamza Merzic; Fabio Viola; Jane Wang 001; Jovana Mitrovic; Frederic Besse; et al. (CoRR, 2020)

Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning. - Giambattista Parascandolo; Lars Buesing; Josh Merel; Leonard Hasenclever; John Aslanides; Jessica B. Hamrick; Nicolas Heess; Alexander Neitz; Theophane Weber (CoRR, 2020)

Physically Embedded Planning Problems: New Challenges for Reinforcement Learning. - 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 (CoRR, 2020)

Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban. - Péter Karkus; Mehdi Mirza; Arthur Guez; Andrew Jaegle; Timothy P. Lillicrap; Lars Buesing; Nicolas Heess; Theophane Weber (CoRR, 2020)

Counterfactual Credit Assignment in Model-Free Reinforcement Learning. - Thomas Mesnard; Théophane Weber; Fabio Viola; Shantanu Thakoor; Alaa Saade; Anna Harutyunyan; Will Dabney; Tom Stepleton; Nicolas Heess; Arthur Guez; Marcus Hutter; Lars Buesing; et al. (CoRR, 2020)

On the role of planning in model-based deep reinforcement learning. - Jessica B. Hamrick; Abram L. Friesen; Feryal M. P. Behbahani; Arthur Guez; Fabio Viola; Sims Witherspoon; Thomas Anthony 001; Lars Holger Buesing; Petar Velickovic; Theophane Weber (ICLR, 2021)

Muesli: Combining Improvements in Policy Optimization. - Matteo Hessel; Ivo Danihelka; Fabio Viola; Arthur Guez; Simon Schmitt; Laurent Sifre; Theophane Weber; David Silver; Hado van Hasselt (ICML, 2021)

Counterfactual Credit Assignment in Model-Free Reinforcement Learning. - 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; et al. (ICML, 2021)

Neural Recursive Belief States in Multi-Agent Reinforcement Learning. - Pol Moreno; Edward Hughes 001; Kevin R. McKee; Bernardo Ávila Pires; Théophane Weber (CoRR, 2021)

Synthetic Returns for Long-Term Credit Assignment. - David Raposo; Samuel Ritter; Adam Santoro; Greg Wayne; Theophane Weber; Matt M. Botvinick; Hado van Hasselt; H. Francis Song (CoRR, 2021)

Procedural generalization by planning with self-supervised world models. - Ankesh Anand; Jacob C. Walker; Yazhe Li; Eszter Vértes; Julian Schrittwieser; Sherjil Ozair; Theophane Weber; Jessica B. Hamrick (ICLR, 2022)

Retrieval-Augmented Reinforcement Learning. - 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; et al. (ICML, 2022)

Large-Scale Retrieval for Reinforcement Learning. - Peter Conway Humphreys; Arthur Guez; Olivier Tieleman; Laurent Sifre; Theophane Weber; Timothy P. Lillicrap (NeurIPS, 2022)

Retrieval-Augmented Reinforcement Learning. - 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; et al. (CoRR, 2022)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane Wang 001; Jörg Bornschein; Theophane Weber; Anirudh Goyal; Matthew M. Botvinick; Michael Mozer; Danilo Jimenez Rezende (CoRR, 2022)

Learning to Induce Causal Structure. - 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 (ICLR, 2023)

Quantile Credit Assignment. - Thomas Mesnard; Wenqi Chen; Alaa Saade; Yunhao Tang; Mark Rowland 001; Theophane Weber; Clare Lyle; Audrunas Gruslys; Michal Valko; Will Dabney; Georg Ostrovski; Eric Moulines; et al. (ICML, 2023)

Investigating the Role of Model-Based Learning in Exploration and Transfer. - Jacob C. Walker; Eszter Vértes; Yazhe Li; Gabriel Dulac-Arnold; Ankesh Anand; Theophane Weber; Jessica B. Hamrick (ICML, 2023)

Laser: Latent Set Representations for 3D Generative Modeling. - Pol Moreno; Adam R. Kosiorek; Heiko Strathmann; Daniel Zoran; Rosália G. Schneider; Björn Winckler; Larisa Markeeva; Théophane Weber; Danilo J. Rezende (CoRR, 2023)

DiscoGen: Learning to Discover Gene Regulatory Networks. - 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; et al. (CoRR, 2023)

Equivariant MuZero. - Andreea Deac; Theophane Weber; George Papamakarios (Trans. Mach. Learn. Res., 2023)

Advancing Geometry with AI: Multi-agent Generation of Polytopes. - Grzegorz Swirszcz; Adam Zsolt Wagner; Geordie Williamson; Sam Blackwell; Bogdan Georgiev; Alex Davies; S. M. Ali Eslami; Sébastien Racanière; Theophane Weber; Pushmeet Kohli (CoRR, 2025)

Drums of high width. - Alex Davies; Prateek Gupta; Sébastien Racanière; Grzegorz Swirszcz; Adam Zsolt Wagner; Theophane Weber; Geordie Williamson (CoRR, 2025)