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02/24/2022
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
The proliferation of computing devices has brought about an opportunity ...
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06/09/2020
Variational Auto-Regressive Gaussian Processes for Continual Learning
This paper proposes Variational Auto-Regressive Gaussian Process (VAR-GP...
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02/10/2020
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Probabilistic neural networks are typically modeled with independent wei...
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05/06/2019
Improving and Understanding Variational Continual Learning
In the continual learning setting, tasks are encountered sequentially. T...
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11/27/2018
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Variational inference (VI) has become the method of choice for fitting m...
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10/29/2017
Variational Continual Learning
This paper develops variational continual learning (VCL), a simple but g...
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05/19/2017
Streaming Sparse Gaussian Process Approximations
Sparse pseudo-point approximations for Gaussian process (GP) models prov...
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03/14/2017
Neural Graph Machines: Learning Neural Networks Using Graphs
Label propagation is a powerful and flexible semi-supervised learning te...
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05/23/2016
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Gaussian processes (GPs) are flexible distributions over functions that ...
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02/12/2016
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisati...
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11/11/2015
