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ORCIDLars Buesing
Person information
- affiliation: Google DeepMind
- affiliation: Columbia University, Grossman Center for the Statistics of Mind
- affiliation: University College London, Gatsby Computational Neuroscience Unit
- affiliation: Graz University of Technology, Institute for Theoretical Computer Science
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2020 – today
- 2023
[c22]Matko Bosnjak, Pierre Harvey Richemond, Nenad Tomasev, Florian Strub, Jacob C. Walker, Felix Hill, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic:
SemPPL: Predicting Pseudo-Labels for Better Contrastive Representations. ICLR 2023- [i19]Matko Bosnjak, Pierre H. Richemond, Nenad Tomasev, Florian Strub, Jacob C. Walker, Felix Hill, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic:
SemPPL: Predicting pseudo-labels for better contrastive representations. CoRR abs/2301.05158 (2023) - 2022
- [c21]Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, Marek J. Sergot:
Making Sense of Raw Input (Extended Abstract). IJCAI 2022: 5727-5731 - [i18]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 abs/2201.05119 (2022) - 2021
- [j9]Richard Evans, Matko Bosnjak, Lars Buesing
, Kevin Ellis, David P. Reichert, Pushmeet Kohli, Marek J. Sergot:
Making sense of raw input. Artif. Intell. 299: 103521 (2021) - [j8]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): 70-74 (2021) - [c20]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 - [c19]Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Holger Buesing, Charles Blundell:
Representation Learning via Invariant Causal Mechanisms. ICLR 2021 - [c18]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
- [c17]Lars Buesing, Nicolas Heess, Theophane Weber:
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. AISTATS 2020: 624-634 - [c16]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 - [c15]Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess:
Value-driven Hindsight Modelling. NeurIPS 2020 - [c14]Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell:
Pointer Graph Networks. NeurIPS 2020 - [i17]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) - [i16]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) - [i15]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) - [i14]Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell:
Pointer Graph Networks. CoRR abs/2006.06380 (2020) - [i13]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) - [i12]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) - [i11]Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Buesing, Charles Blundell:
Representation Learning via Invariant Causal Mechanisms. CoRR abs/2010.07922 (2020) - [i10]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) - [i9]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)
2010 – 2019
- 2019
- [c13]Théophane Weber, Nicolas Heess, Lars Buesing, David Silver:
Credit Assignment Techniques in Stochastic Computation Graphs. AISTATS 2019: 2650-2660 - [c12]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 - [c11]Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber:
Temporal Difference Variational Auto-Encoder. ICLR 2019 - [i8]Théophane Weber, Nicolas Heess, Lars Buesing, David Silver:
Credit Assignment Techniques in Stochastic Computation Graphs. CoRR abs/1901.01761 (2019) - [i7]Lars Buesing, Nicolas Heess, Theophane Weber:
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. CoRR abs/1910.06862 (2019) - [i6]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
- [i5]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) - [i4]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) - 2017
- [c10]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: 4024-4034 - [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 - [i3]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) - [i2]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) - [i1]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 abs/1711.01846 (2017) - 2015
- [c8]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: 154-162 - [c7]Yuanjun Gao, Lars Buesing, Krishna V. Shenoy, John P. Cunningham:
High-dimensional neural spike train analysis with generalized count linear dynamical systems. NIPS 2015: 2044-2052 - 2014
- [c6]Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski:
Clustered factor analysis of multineuronal spike data. NIPS 2014: 3500-3508 - 2013
- [j7]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) - [c5]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: 539-547 - 2012
- [c4]Lars Buesing, Jakob H. Macke, Maneesh Sahani:
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 2012: 1691-1699 - 2011
- [j6]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) - [j5]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) - [c3]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: 1350-1358 - 2010
- [j4]Lars Büsing, Benjamin Schrauwen, Robert Legenstein:
Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons. Neural Comput. 22(5): 1272-1311 (2010) - [j3]Lars Buesing, Wolfgang Maass:
A Spiking Neuron as Information Bottleneck. Neural Comput. 22(8): 1961-1992 (2010)
2000 – 2009
- 2008
- [j2]Claudia Clopath, Lorric Ziegler, Eleni Vasilaki, Lars Büsing, Wulfram Gerstner:
Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression. PLoS Comput. Biol. 4(12) (2008) - [c2]Benjamin Schrauwen, Lars Buesing, Robert Legenstein:
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. NIPS 2008: 1425-1432 - 2007
- [j1]Eilif Mueller, Lars Buesing, Johannes Schemmel, Karlheinz Meier:
Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories. Neural Comput. 19(11): 2958-3010 (2007) - [c1]Lars Buesing, Wolfgang Maass:
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons. NIPS 2007: 193-200
Coauthor Index
aka: Théophane Weber
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