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06/12/2023
Riemannian Laplace approximations for Bayesian neural networks
Bayesian neural networks often approximate the weight-posterior with a G...
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06/01/2023
On Masked Pre-training and the Marginal Likelihood
Masked pre-training removes random input dimensions and learns a model t...
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09/10/2022
Revisiting Active Sets for Gaussian Process Decoders
Decoders built on Gaussian processes (GPs) are enticing due to the margi...
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06/30/2022
Laplacian Autoencoders for Learning Stochastic Representations
Established methods for unsupervised representation learning such as var...
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02/22/2022
Adaptive Cholesky Gaussian Processes
We present a method to fit exact Gaussian process models to large datase...
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10/26/2021
Modular Gaussian Processes for Transfer Learning
We present a framework for transfer learning based on modular variationa...
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11/14/2020
Passive detection of behavioral shifts for suicide attempt prevention
More than one million people commit suicide every year worldwide. The co...
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10/06/2020
Recyclable Gaussian Processes
We present a new framework for recycling independent variational approxi...
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07/24/2020
Multinomial Sampling for Hierarchical Change-Point Detection
Bayesian change-point detection, together with latent variable models, a...
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10/31/2019
Continual Multi-task Gaussian Processes
We address the problem of continual learning in multi-task Gaussian proc...
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10/22/2019
Continual Learning for Infinite Hierarchical Change-Point Detection
Change-point detection (CPD) aims to locate abrupt transitions in the ge...
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09/11/2018
Change-Point Detection on Hierarchical Circadian Models
This paper addresses the problem of change-point detection on sequences ...
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05/19/2018