Lars Buesing

Publication Activity (10 Years)

Years Active: 2007-2022
Publications (10 Years): 33

Top Topics

Credit Assignment

Monte Carlo Tree Search

Approximate Inference

Temporal Difference

Top Venues

Publications

Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? CoRR (2022)
Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, Marek J. Sergot
Making Sense of Raw Input (Extended Abstract). IJCAI (2022)
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)
Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David P. Reichert, Pushmeet Kohli, Marek J. Sergot
Making sense of raw input. Artif. Intell. 299 (2021)
Alex Davies, Petar Velickovic, Lars Buesing, Sam Blackwell, Daniel Zheng, Nenad Tomasev, Richard Tanburn, Peter W. Battaglia, Charles Blundell, András Juhász, Marc Lackenby, Geordie Williamson, Demis Hassabis, Pushmeet Kohli
Advancing mathematics by guiding human intuition with AI. Nat. 600 (7887) (2021)
Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess
Value-driven Hindsight Modelling. CoRR (2020)
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)
Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell
Pointer Graph Networks. CoRR (2020)
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 (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)
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 (2020)
Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell
Pointer Graph Networks. NeurIPS (2020)
Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Buesing, Charles Blundell
Representation Learning via Invariant Causal Mechanisms. CoRR (2020)
Jessica B. Hamrick, Abram L. Friesen, Feryal 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 (2020)
Théophane Weber, Nicolas Heess, Lars Buesing, David Silver
Credit Assignment Techniques in Stochastic Computation Graphs. CoRR (2019)
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)
Théophane Weber, Nicolas Heess, Lars Buesing, David Silver
Credit Assignment Techniques in Stochastic Computation Graphs. AISTATS (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)
Lars Buesing, Theophane Weber, Yori Zwols, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. 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)
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)
Artur Speiser, Jinyao Yan, Evan W. Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. NIPS (2017)
Artur Speiser, Jinyao Yan, Evan Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. 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)
Yuanjun Gao, Lars Buesing, Krishna V. Shenoy, John P. Cunningham
High-dimensional neural spike train analysis with generalized count linear dynamical systems. NIPS (2015)
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltán Szabó, Lars Buesing, Maneesh Sahani
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). NIPS (2015)
Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
Clustered factor analysis of multineuronal spike data. NIPS (2014)
Srinivas C. Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Häusser, Jakob H. Macke
Inferring neural population dynamics from multiple partial recordings of the same neural circuit. NIPS (2013)
Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity. PLoS Comput. Biol. 9 (4) (2013)
Lars Buesing, Jakob H. Macke, Maneesh Sahani
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS (2012)
Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
Empirical models of spiking in neural populations. NIPS (2011)
Dejan Pecevski, Lars Buesing, Wolfgang Maass
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons. PLoS Comput. Biol. 7 (12) (2011)
Lars Buesing, Johannes Bill, Bernhard Nessler, Wolfgang Maass
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons. PLoS Comput. Biol. 7 (11) (2011)
Lars Buesing, Wolfgang Maass
A Spiking Neuron as Information Bottleneck. Neural Comput. 22 (8) (2010)
Benjamin Schrauwen, Lars Buesing, Robert Legenstein
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. NIPS (2008)
Eilif Mueller, Lars Buesing, Johannes Schemmel, Karlheinz Meier
Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories. Neural Comput. 19 (11) (2007)
Lars Buesing, Wolfgang Maass
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons. NIPS (2007)