Michalis K. Titsias

research

∙ 06/14/2023

Kalman Filter for Online Classification of Non-Stationary Data

In Online Continual Learning (OCL) a learning system receives a stream o...

0 Michalis K. Titsias, et al. ∙

research

∙ 05/23/2023

Optimal Preconditioning and Fisher Adaptive Langevin Sampling

We define an optimal preconditioning for the Langevin diffusion by analy...

0 Michalis K. Titsias, et al. ∙

research

∙ 03/04/2022

Variance Reduction for Metropolis-Hastings Samplers

We introduce a general framework that constructs estimators with reduced...

0 Angelos Alexopoulos, et al. ∙

research

∙ 02/20/2022

Personalized Federated Learning with Exact Stochastic Gradient Descent

In Federated Learning (FL), datasets across clients tend to be heterogen...

0 Sotirios Nikoloutsopoulos, et al. ∙

research

∙ 02/19/2022

Gradient Estimation with Discrete Stein Operators

Gradient estimation – approximating the gradient of an expectation with ...

0 Jiaxin Shi, et al. ∙

research

∙ 11/09/2021

Double Control Variates for Gradient Estimation in Discrete Latent Variable Models

Stochastic gradient-based optimisation for discrete latent variable mode...

0 Michalis K. Titsias, et al. ∙

research

∙ 10/27/2021

Entropy-based adaptive Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCM...

0 Marcel Hirt, et al. ∙

research

∙ 10/06/2020

Sequential Changepoint Detection in Neural Networks with Checkpoints

We introduce a framework for online changepoint detection and simultaneo...

0 Michalis K. Titsias, et al. ∙

research

∙ 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 ...

9 Francisco J. R. Ruiz, et al. ∙

research

∙ 09/07/2020

Information Theoretic Meta Learning with Gaussian Processes

We formulate meta learning using information theoretic concepts such as ...

0 Michalis K. Titsias, et al. ∙

research

∙ 11/04/2019

Gradient-based Adaptive Markov Chain Monte Carlo

We introduce a gradient-based learning method to automatically adapt Mar...

26 Michalis K. Titsias, et al. ∙

research

∙ 10/23/2019

Sparse Orthogonal Variational Inference for Gaussian Processes

We introduce a new interpretation of sparse variational approximations f...

0 Jiaxin Shi, et al. ∙

research

∙ 10/09/2019

Prescribed Generative Adversarial Networks

Generative adversarial networks (GANs) are a powerful approach to unsupe...

25 Adji B. Dieng, et al. ∙

research

∙ 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...

0 Francisco J. R. Ruiz, et al. ∙

research

∙ 01/31/2019

Functional Regularisation for Continual Learning using Gaussian Processes

We introduce a novel approach for supervised continual learning based on...

14 Michalis K. Titsias, et al. ∙

research

∙ 09/30/2018

Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules

We propose a probabilistic framework to directly insert prior knowledge ...

0 Michalis K. Titsias, et al. ∙

research

∙ 08/06/2018

Unbiased Implicit Variational Inference

We develop unbiased implicit variational inference (UIVI), a method that...

0 Michalis K. Titsias, et al. ∙

research

∙ 07/06/2018

Fully Scalable Gaussian Processes using Subspace Inducing Inputs

We introduce fully scalable Gaussian processes, an implementation scheme...

0 Aristeidis Panos, et al. ∙

research

∙ 02/12/2018

Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Categorical distributions are ubiquitous in machine learning, e.g., in c...

0 Francisco J. R. Ruiz, et al. ∙

research

∙ 08/04/2017

Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions

We introduce a new algorithm for approximate inference that combines rep...

0 Michalis K. Titsias, et al. ∙

research

∙ 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 ...

0 Kaspar Märtens, et al. ∙

research

∙ 02/20/2017

Bayesian Boolean Matrix Factorisation

Boolean matrix factorisation aims to decompose a binary data matrix into...

0 Tammo Rukat, et al. ∙

research

∙ 10/30/2016

Auxiliary gradient-based sampling algorithms

We introduce a new family of MCMC samplers that combine auxiliary variab...

0 Michalis K. Titsias, et al. ∙

research

∙ 10/07/2016

The Generalized Reparameterization Gradient

The reparameterization gradient has become a widely used method to obtai...

0 Francisco J. R. Ruiz, et al. ∙

research

∙ 09/23/2016

One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities

The softmax representation of probabilities for categorical variables pl...

0 Michalis K. Titsias, et al. ∙

research

∙ 03/03/2016

Overdispersed Black-Box Variational Inference

We introduce overdispersed black-box variational inference, a method to ...

0 Francisco J. R. Ruiz, et al. ∙

research

∙ 07/04/2015

Inference for determinantal point processes without spectral knowledge

Determinantal point processes (DPPs) are point process models that natur...

0 Rémi Bardenet, et al. ∙

research

∙ 03/04/2015

Local Expectation Gradients for Doubly Stochastic Variational Inference

We introduce local expectation gradients which is a general purpose stoc...

0 Michalis K. Titsias, et al. ∙

research

∙ 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 ...

0 Andreas C. Damianou, et al. ∙

research

∙ 07/25/2011

Variational Gaussian Process Dynamical Systems

High dimensional time series are endemic in applications of machine lear...

0 Andreas C. Damianou, et al. ∙

research

∙ 12/16/2009

Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes

Interest in multioutput kernel methods is increasing, whether under the ...

0 Mauricio A. Álvarez, et al. ∙

Michalis K. Titsias

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