Probabilistic learning

Publication

Denoising diffusion samplers

Francisco Vargas, Will Grathwohl, Arnaud Doucet

ICLR

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Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation

Antoine Dedieu, Stannis Zhou, Dileep George, Miguel Lazaro-Gredilla

arXiv

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Particle-Based Score Estimation for Jointly Learning Transition and Observation Models in Autonomous Driving

Angad Singh *, Omar Makhlouf *, Maximilian Igl *, Joao Messias *, Arnaud Doucet, Shimon Whiteson *

CoRL

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Score-based generative models learn manifold-like structures with constrained mixing

Kevin Li, Ben Moran

NeurIPS Workshop

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Unbiased and Efficient Sampling of Dependency Trees

Miloš Stanojević

EMNLP

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Score-Based Diffusion meets Annealed Importance Sampling

Arnaud Doucet, Will Grathwohl, Alex Matthews, Heiko Strathmann

NeurIPS

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Beyond Bayes-optimality: meta-learning what you know you don’t know

Jordi Grau, Grégoire Delétang, Markus Kunesch, Tim Genewein, Kevin Li, Elliot Catt, Anian Ruoss, Chris Cundy, Joel Veness, Jane Wang, Marcus Hutter, Christopher Summerfield, Shane Legg

arXiv

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Importance Weighting Approach in Kernel Bayes' Rule

Liyuan Xu *, Yutian Chen, Arnaud Doucet, Arthur Gretton *

ICML

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Annealed Importance Sampling Meets Score Matching

Arnaud Doucet, Will Grathwohl, Alex Matthews, Heiko Strathmann

ICLR

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Learning Optimal Conformal Classifiers

David Stutz, Taylan Cemgil, Krishnamurthy Dvijotham *, Arnaud Doucet

ICLR

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Fast sampling from β-ensembles

Guillaume Gautier *, R Bardenet *, Michal Valko

Statistics and Computing Journal

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Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains

F Ruiz *, Michalis Titsias, Taylan Cemgil, Arnaud Doucet

arXiv

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Information Theoretic Meta Learning with Gaussian Processes

Michalis Titsias, Sotirios Nikoloutsopoulos *, Alexandre Galashov

arXiv

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Action and Perception as Divergence Minimization

Danijar Hafner *, Pedro A. Ortega, Jimmy Ba *, Thomas Parr *, Karl Friston *, Nicolas Heess

arXiv

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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

Mike Dusenberry *, G Jerfel *, Y Wen *, Y Ma *, Jasper Snoek *, Katherine Heller *, Balaji Lakshminarayanan, D Tran *

ICML

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Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning

Giambattista Parascandolo *, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jess Hamrick, Nicolas Heess, Alexander Neitz *, Theophane Weber

arXiv

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Static and Dynamic Values of Computation in MCTS

Eren Sezener, Peter Dayan *

arXiv

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On Contrastive Learning for Likelihood-free Inference

Conor Durkan *, Iain Murray *, George Papamakarios

ICML

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Causally Correct Partial Models for Reinforcement Learning

Danilo Rezende, Ivo Danihelka, George Papamakarios, N R Ke *, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing

arXiv

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Normalizing Flows on Tori and Spheres

Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael Albergo *, Gurtej Kanwar *, Phiala Shanahan *, K Cranmer *

ICML

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Product Kanerva Machines: Factorized Bayesian Memory

Adam Marblestone, Yan Wu, Gregory Wayne

arXiv

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