Lars Buesing - RankMe

45 papers A* 5A 2Journal 26Unranked 12

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Amplifying human performance in combinatorial competitive programming. CoRR Petar Velickovic, Alex Vitvitskyi, Larisa Markeeva, Borja Ibarz, Lars Buesing, Matej Balog, Alexander Novikov
2022	A*	`conf`	Making Sense of Raw Input (Extended Abstract). IJCAI Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, Marek J. Sergot
2022	J	`jnl`	Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? CoRR Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic
2021	J	`jnl`	Advancing mathematics by guiding human intuition with AI. Nat. 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
2021	A*	`conf`	Counterfactual Credit Assignment in Model-Free Reinforcement Learning. ICML 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
2021	J	`jnl`	Making sense of raw input. Artif. Intell. Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David P. Reichert, Pushmeet Kohli, Marek J. Sergot
2020	A	`conf`	Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. AISTATS Lars Buesing, Nicolas Heess, Theophane Weber
2020	J	`jnl`	Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban. CoRR Péter Karkus, Mehdi Mirza, Arthur Guez, Andrew Jaegle, Timothy P. Lillicrap, Lars Buesing, Nicolas Heess, Theophane Weber
2020	J	`jnl`	Causally Correct Partial Models for Reinforcement Learning. CoRR 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
2020	A*	`conf`	Combining Q-Learning and Search with Amortized Value Estimates. ICLR Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, Peter W. Battaglia
2020	J	`jnl`	Counterfactual Credit Assignment in Model-Free Reinforcement Learning. CoRR 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
2020	J	`jnl`	Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning. CoRR Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber
2020	J	`jnl`	On the role of planning in model-based deep reinforcement learning. CoRR Jessica B. Hamrick, Abram L. Friesen, Feryal M. P. Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Velickovic, Théophane Weber
2020	J	`jnl`	Physically Embedded Planning Problems: New Challenges for Reinforcement Learning. CoRR 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
2020	A*	`conf`	Pointer Graph Networks. NeurIPS Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell
2020	J	`jnl`	Pointer Graph Networks. CoRR Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell
2020	J	`jnl`	Representation Learning via Invariant Causal Mechanisms. CoRR Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Buesing, Charles Blundell
2020	A*	`conf`	Value-driven Hindsight Modelling. NeurIPS Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess
2020	J	`jnl`	Value-driven Hindsight Modelling. CoRR Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess
2019	J	`jnl`	Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. CoRR Lars Buesing, Nicolas Heess, Theophane Weber
2019	J	`jnl`	Combining Q-Learning and Search with Amortized Value Estimates. CoRR Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, Peter W. Battaglia
2019	A	`conf`	Credit Assignment Techniques in Stochastic Computation Graphs. AISTATS Théophane Weber, Nicolas Heess, Lars Buesing, David Silver
2019	J	`jnl`	Credit Assignment Techniques in Stochastic Computation Graphs. CoRR Théophane Weber, Nicolas Heess, Lars Buesing, David Silver
2019	—	`conf`	Temporal Difference Variational Auto-Encoder. ICLR Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber
2019	—	`conf`	Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. ICLR (Poster) Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau
2018	J	`jnl`	Learning and Querying Fast Generative Models for Reinforcement Learning. CoRR 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
2018	J	`jnl`	Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. CoRR Lars Buesing, Theophane Weber, Yori Zwols, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess
2017	—	`conf`	Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. NIPS Artur Speiser, Jinyao Yan, Evan W. Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke
2017	J	`jnl`	Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. CoRR Artur Speiser, Jinyao Yan, Evan Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke
2017	—	`conf`	Imagination-Augmented Agents for Deep Reinforcement Learning. NIPS 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
2017	J	`jnl`	Imagination-Augmented Agents for Deep Reinforcement Learning. CoRR 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
2017	J	`jnl`	Learning model-based planning from scratch. CoRR Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sébastien Racanière, David P. Reichert, Theophane Weber, Daan Wierstra, Peter W. Battaglia
2015	—	`conf`	Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). NIPS Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltán Szabó, Lars Buesing, Maneesh Sahani
2015	—	`conf`	High-dimensional neural spike train analysis with generalized count linear dynamical systems. NIPS Yuanjun Gao, Lars Buesing, Krishna V. Shenoy, John P. Cunningham
2014	—	`conf`	Clustered factor analysis of multineuronal spike data. NIPS Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
2013	J	`jnl`	Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity. PLoS Comput. Biol. Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass
2013	—	`conf`	Inferring neural population dynamics from multiple partial recordings of the same neural circuit. NIPS Srinivas C. Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Häusser, Jakob H. Macke
2012	—	`conf`	Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS Lars Buesing, Jakob H. Macke, Maneesh Sahani
2011	—	`conf`	Empirical models of spiking in neural populations. NIPS Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
2011	J	`jnl`	Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons. PLoS Comput. Biol. Lars Buesing, Johannes Bill, Bernhard Nessler, Wolfgang Maass
2011	J	`jnl`	Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons. PLoS Comput. Biol. Dejan Pecevski, Lars Buesing, Wolfgang Maass
2010	J	`jnl`	A Spiking Neuron as Information Bottleneck. Neural Comput. Lars Buesing, Wolfgang Maass
2008	—	`conf`	On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. NIPS Benjamin Schrauwen, Lars Buesing, Robert Legenstein
2007	—	`conf`	Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons. NIPS Lars Buesing, Wolfgang Maass
2007	J	`jnl`	Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories. Neural Comput. Eilif B. Muller, Lars Buesing, Johannes Schemmel, Karlheinz Meier