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