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Tue 0:00 Framing RNN as a kernel method: A neural ODE approach
Adeline Fermanian · Pierre Marion · Jean-Philippe Vert · Gérard Biau
Tue 8:30 BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau · Jean-Francois Ton · Javier González · Yee Teh · Dino Sejdinovic
Tue 8:30 Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Michael Hutchinson · Alexander Terenin · Viacheslav Borovitskiy · So Takao · Yee Teh · Marc Deisenroth
Tue 8:30 Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
Feng Liu · Wenkai Xu · Jie Lu · Danica J. Sutherland
Tue 8:30 Provably Strict Generalisation Benefit for Invariance in Kernel Methods
Bryn Elesedy
Tue 8:30 Black Box Probabilistic Numerics
Onur Teymur · Christopher Foley · Philip Breen · Toni Karvonen · Chris Oates
Tue 8:30 Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Vincent Dutordoir · James Hensman · Mark van der Wilk · Carl Henrik Ek · Zoubin Ghahramani · Nicolas Durrande
Tue 8:30 A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
Sudeep Salgia · Sattar Vakili · Qing Zhao
Tue 8:30 Spatio-Temporal Variational Gaussian Processes
Oliver Hamelijnck · William Wilkinson · Niki Loppi · Arno Solin · Theodoros Damoulas
Tue 8:30 A Kernel-based Test of Independence for Cluster-correlated Data
Hongjiao Liu · Anna Plantinga · Yunhua Xiang · Michael Wu
Tue 8:30 Locality defeats the curse of dimensionality in convolutional teacher-student scenarios
Alessandro Favero · Francesco Cagnetta · Matthieu Wyart
Tue 8:30 A variational approximate posterior for the deep Wishart process
Sebastian Ober · Laurence Aitchison