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06/14/2023
Kalman Filter for Online Classification of Non-Stationary Data
In Online Continual Learning (OCL) a learning system receives a stream o...
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05/23/2023
Optimal Preconditioning and Fisher Adaptive Langevin Sampling
We define an optimal preconditioning for the Langevin diffusion by analy...
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03/04/2022
Variance Reduction for Metropolis-Hastings Samplers
We introduce a general framework that constructs estimators with reduced...
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02/20/2022
Personalized Federated Learning with Exact Stochastic Gradient Descent
In Federated Learning (FL), datasets across clients tend to be heterogen...
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02/19/2022
Gradient Estimation with Discrete Stein Operators
Gradient estimation – approximating the gradient of an expectation with ...
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11/09/2021
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models
Stochastic gradient-based optimisation for discrete latent variable mode...
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10/27/2021
Entropy-based adaptive Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCM...
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10/06/2020
Sequential Changepoint Detection in Neural Networks with Checkpoints
We introduce a framework for online changepoint detection and simultaneo...
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10/05/2020
Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains
The variational auto-encoder (VAE) is a deep latent variable model that ...
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09/07/2020
Information Theoretic Meta Learning with Gaussian Processes
We formulate meta learning using information theoretic concepts such as ...
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11/04/2019
Gradient-based Adaptive Markov Chain Monte Carlo
We introduce a gradient-based learning method to automatically adapt Mar...
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10/23/2019
Sparse Orthogonal Variational Inference for Gaussian Processes
We introduce a new interpretation of sparse variational approximations f...
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10/09/2019
Prescribed Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful approach to unsupe...
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05/10/2019
A Contrastive Divergence for Combining Variational Inference and MCMC
We develop a method to combine Markov chain Monte Carlo (MCMC) and varia...
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01/31/2019
Functional Regularisation for Continual Learning using Gaussian Processes
We introduce a novel approach for supervised continual learning based on...
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09/30/2018
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules
We propose a probabilistic framework to directly insert prior knowledge ...
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08/06/2018
Unbiased Implicit Variational Inference
We develop unbiased implicit variational inference (UIVI), a method that...
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07/06/2018
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
We introduce fully scalable Gaussian processes, an implementation scheme...
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02/12/2018
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in c...
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08/04/2017
Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions
We introduce a new algorithm for approximate inference that combines rep...
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03/24/2017
Rejection-free Ensemble MCMC with applications to Factorial Hidden Markov Models
Bayesian inference for complex models is challenging due to the need to ...
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02/20/2017
Bayesian Boolean Matrix Factorisation
Boolean matrix factorisation aims to decompose a binary data matrix into...
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10/30/2016
Auxiliary gradient-based sampling algorithms
We introduce a new family of MCMC samplers that combine auxiliary variab...
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10/07/2016
The Generalized Reparameterization Gradient
The reparameterization gradient has become a widely used method to obtai...
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09/23/2016
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
The softmax representation of probabilities for categorical variables pl...
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03/03/2016
Overdispersed Black-Box Variational Inference
We introduce overdispersed black-box variational inference, a method to ...
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07/04/2015
Inference for determinantal point processes without spectral knowledge
Determinantal point processes (DPPs) are point process models that natur...
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03/04/2015
Local Expectation Gradients for Doubly Stochastic Variational Inference
We introduce local expectation gradients which is a general purpose stoc...
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09/08/2014
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
The Gaussian process latent variable model (GP-LVM) provides a flexible ...
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07/25/2011
Variational Gaussian Process Dynamical Systems
High dimensional time series are endemic in applications of machine lear...
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12/16/2009
