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02/20/2023
Free-Form Variational Inference for Gaussian Process State-Space Models
Gaussian process state-space models (GPSSMs) provide a principled and fl...
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11/01/2022
Recurrent Neural Networks and Universal Approximation of Bayesian Filters
We consider the Bayesian optimal filtering problem: i.e. estimating some...
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02/25/2022
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
Learning useful node and graph representations with graph neural network...
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07/04/2021
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Advances in differentiable numerical integrators have enabled the use of...
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06/11/2021
Model Selection for Bayesian Autoencoders
We develop a novel method for carrying out model selection for Bayesian ...
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05/10/2021
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Making predictions and quantifying their uncertainty when the input data...
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02/17/2021
BORE: Bayesian Optimization by Density-Ratio Estimation
Bayesian optimization (BO) is among the most effective and widely-used b...
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12/21/2019
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
In this work, we develop a new approximation method to solve the analyti...
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06/07/2019
Structured Variational Inference in Continuous Cox Process Models
We propose a scalable framework for inference in an inhomogeneous Poisso...
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06/05/2019
Variational Spectral Graph Convolutional Networks
We propose a Bayesian approach to spectral graph convolutional networks ...
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03/10/2019
Sparse Grouped Gaussian Processes for Solar Power Forecasting
We consider multi-task regression models where observations are assumed ...
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06/07/2018
Grouped Gaussian Processes for Solar Power Prediction
We consider multi-task regression models where the observations are assu...
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06/05/2018
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
We formalize the problem of learning interdomain correspondences in the ...
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05/26/2018
Calibrating Deep Convolutional Gaussian Processes
The wide adoption of Convolutional Neural Networks (CNNs) in application...
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02/27/2017
Semi-parametric Network Structure Discovery Models
We propose a network structure discovery model for continuous observatio...
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10/18/2016
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
We investigate the capabilities and limitations of Gaussian process mode...
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10/14/2016
Random Feature Expansions for Deep Gaussian Processes
The composition of multiple Gaussian Processes as a Deep Gaussian Proces...
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09/14/2016
Gray-box inference for structured Gaussian process models
We develop an automated variational inference method for Bayesian struct...
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09/02/2016