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Oral
Tue 10:45 Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Leena Chennuru Vankadara · Sebastian Bordt · Ulrike von Luxburg · Debarghya Ghoshdastidar
Oral
Tue 11:30 Couplings for Multinomial Hamiltonian Monte Carlo
Kai Xu · Tor Erlend Fjelde · Charles Sutton · Hong Ge
Poster
Tue 14:00 Fast Adaptation with Linearized Neural Networks
Wesley Maddox · Shuai Tang · Pablo Moreno · Andrew Gordon Wilson · Andreas Damianou
Poster
Tue 14:00 Learning GPLVM with arbitrary kernels using the unscented transformation
Daniel Augusto Ramos Macedo Antunes de Souza · Diego Mesquita · João Paulo Gomes · César Lincoln Mattos
Poster
Tue 14:00 Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning
Hitesh Sapkota · Yiming Ying · Feng Chen · Qi Yu
Poster
Tue 14:00 The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
Fergus Simpson · Alexis Boukouvalas · Vaclav Cadek · Elvijs Sarkans · Nicolas Durrande
Poster
Tue 14:00 Matérn Gaussian Processes on Graphs
Viacheslav Borovitskiy · Iskander Azangulov · Alexander Terenin · Peter Mostowsky · Marc Deisenroth · Nicolas Durrande
Poster
Tue 14:00 No-Regret Algorithms for Private Gaussian Process Bandit Optimization
Abhimanyu Dubey
Oral
Tue 17:45 Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
Chaoqi Wang · Shengyang Sun · Roger Grosse
Poster
Tue 18:30 Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
Chaoqi Wang · Shengyang Sun · Roger Grosse
Poster
Tue 18:30 Mean-Variance Analysis in Bayesian Optimization under Uncertainty
Shogo Iwazaki · Yu Inatsu · Ichiro Takeuchi
Poster
Tue 18:30 Hierarchical Inducing Point Gaussian Process for Inter-domian Observations
Luhuan Wu · Andrew Miller · Lauren Anderson · Geoff Pleiss · David Blei · John Cunningham