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Workshop
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Gaussian Process-Based Representation Learning via Timeseries Symmetries
Petar Bevanda · Max Beier · Armin Lederer · Alexandre Capone · Stefan Sosnowski · Sandra Hirche
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Workshop
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Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks
Tristan Cinquin · Robert Bamler
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Poster
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Wed 4:30
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Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
Yingyi Chen · Qinghua Tao · Francesco Tonin · Johan Suykens
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Workshop
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Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders
Christian Toth · Christian Knoll · Franz Pernkopf · Robert Peharz
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Poster
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Wed 2:30
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Amortized Variational Deep Kernel Learning
Alan Matias · César Lincoln Mattos · Joao Paulo Gomes · Diego Mesquita
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Workshop
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Heterogeneous Federated Zeroth-Order Optimization using Gradient Surrogates
Yao Shu · Xiaoqiang Lin · Zhongxiang Dai · Bryan Kian Hsiang Low
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Poster
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Tue 2:30
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Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
Weihan Li · Chengrui Li · Yule Wang · Anqi Wu
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Poster
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Wed 2:30
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Preventing Model Collapse in Gaussian Process Latent Variable Models
Ying Li · Zhidi Lin · Feng Yin · Michael Minyi Zhang
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Workshop
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Approximate natural gradient in Gaussian processes with non-log-concave likelihoods
Marcelo Hartmann
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Workshop
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Learning high-dimensional mixed models via amortized variational inference
Priscilla Ong · Manuel Haussmann · Harri Lähdesmäki
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Poster
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Wed 2:30
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Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic
Nicolas Alder · Ralf Herbrich
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Poster
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Tue 4:30
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Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds
Noémie Jaquier · Leonel Rozo · Miguel González-Duque · Slava Borovitskiy · Tamim Asfour
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