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Lars Buesing

18 papers · 2007–2022 · 5 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (15) 🐝 Cross-Pollinator (15) 🌟 Keyword Trendsetter Combo (3) 🧬 Topic Evolution πŸ”₯ Unstoppable (5) πŸ“ˆ Trend Setter πŸ’Ž Century Club (18) πŸ—ƒοΈ Keyword Collector (89)

Conferences

NIPS (11) ICLR (3) AISTATS (2) ICML (1) IJCAI (1)

Top co-authors

Theophane Weber (8) Nicolas Heess (6) Arthur Guez (4) Jakob H. Macke (4) David Silver (3) Maneesh Sahani (3) John P. Cunningham (2) Sébastien Racanière (2) Fabio Viola (2) Razvan Pascanu (2)

Keywords

value function (4) neural population (3) variational inference (3) spiking neuron (3) calcium imaging (3) dimensionality reduction (2) bayesian inference (2) generalized linear model (2) credit assignment (2) model-free reinforcement learning (2) latent variable model (2) variance reduction (2) dynamical system (2) policy gradient (1) information bottleneck (1) approximate inference (1) statistical modeling (1) online learning (1) principal component analysis (1) theoretical analysis (1)

Papers

Making Sense of Raw Input (Extended Abstract) IJCAI 2022
Counterfactual Credit Assignment in Model-Free Reinforcement Learning ICML 2021
Pointer Graph Networks NIPS 2020
Value-driven Hindsight Modelling NIPS 2020
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions AISTATS 2020
Combining Q-Learning and Search with Amortized Value Estimates ICLR 2020
Temporal Difference Variational Auto-Encoder ICLR 2019
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search ICLR 2019
Credit Assignment Techniques in Stochastic Computation Graphs AISTATS 2019
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders NIPS 2017
Imagination-Augmented Agents for Deep Reinforcement Learning NIPS 2017
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) NIPS 2015
Clustered factor analysis of multineuronal spike data NIPS 2014
Inferring neural population dynamics from multiple partial recordings of the same neural circuit NIPS 2013
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data NIPS 2012
Empirical models of spiking in neural populations NIPS 2011
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing NIPS 2008
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons NIPS 2007