
Gradientbased Adaptive Markov Chain Monte Carlo
We introduce a gradientbased learning method to automatically adapt Mar...
11/04/2019 ∙ by Michalis K. Titsias, et al. ∙ 26 ∙ shareread it

Prescribed Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful approach to unsupe...
10/09/2019 ∙ by Adji B. Dieng, et al. ∙ 25 ∙ shareread it

Functional Regularisation for Continual Learning using Gaussian Processes
We introduce a novel approach for supervised continual learning based on...
01/31/2019 ∙ by Michalis K. Titsias, et al. ∙ 14 ∙ shareread it

Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions
We introduce a new algorithm for approximate inference that combines rep...
08/04/2017 ∙ by Michalis K. Titsias, et al. ∙ 0 ∙ shareread it

Rejectionfree Ensemble MCMC with applications to Factorial Hidden Markov Models
Bayesian inference for complex models is challenging due to the need to ...
03/24/2017 ∙ by Kaspar Märtens, et al. ∙ 0 ∙ shareread it

Auxiliary gradientbased sampling algorithms
We introduce a new family of MCMC samplers that combine auxiliary variab...
10/30/2016 ∙ by Michalis K. Titsias, et al. ∙ 0 ∙ shareread it

The Generalized Reparameterization Gradient
The reparameterization gradient has become a widely used method to obtai...
10/07/2016 ∙ by Francisco J. R. Ruiz, et al. ∙ 0 ∙ shareread it

OnevsEach Approximation to Softmax for Scalable Estimation of Probabilities
The softmax representation of probabilities for categorical variables pl...
09/23/2016 ∙ by Michalis K. Titsias, et al. ∙ 0 ∙ shareread it

Overdispersed BlackBox Variational Inference
We introduce overdispersed blackbox variational inference, a method to ...
03/03/2016 ∙ by Francisco J. R. Ruiz, et al. ∙ 0 ∙ shareread it

Inference for determinantal point processes without spectral knowledge
Determinantal point processes (DPPs) are point process models that natur...
07/04/2015 ∙ by Rémi Bardenet, et al. ∙ 0 ∙ shareread it

Local Expectation Gradients for Doubly Stochastic Variational Inference
We introduce local expectation gradients which is a general purpose stoc...
03/04/2015 ∙ by Michalis K. Titsias, et al. ∙ 0 ∙ shareread it

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
The Gaussian process latent variable model (GPLVM) provides a flexible ...
09/08/2014 ∙ by Andreas C. Damianou, et al. ∙ 0 ∙ shareread it

Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes
Interest in multioutput kernel methods is increasing, whether under the ...
12/16/2009 ∙ by Mauricio A. Álvarez, et al. ∙ 0 ∙ shareread it

Variational Gaussian Process Dynamical Systems
High dimensional time series are endemic in applications of machine lear...
07/25/2011 ∙ by Andreas C. Damianou, et al. ∙ 0 ∙ shareread it

Bayesian Boolean Matrix Factorisation
Boolean matrix factorisation aims to decompose a binary data matrix into...
02/20/2017 ∙ by Tammo Rukat, et al. ∙ 0 ∙ shareread it

Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in c...
02/12/2018 ∙ by Francisco J. R. Ruiz, et al. ∙ 0 ∙ shareread it

Fully Scalable Gaussian Processes using Subspace Inducing Inputs
We introduce fully scalable Gaussian processes, an implementation scheme...
07/06/2018 ∙ by Aristeidis Panos, et al. ∙ 0 ∙ shareread it

Unbiased Implicit Variational Inference
We develop unbiased implicit variational inference (UIVI), a method that...
08/06/2018 ∙ by Michalis K. Titsias, et al. ∙ 0 ∙ shareread it

Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules
We propose a probabilistic framework to directly insert prior knowledge ...
09/30/2018 ∙ by Michalis K. Titsias, et al. ∙ 0 ∙ shareread it

A Contrastive Divergence for Combining Variational Inference and MCMC
We develop a method to combine Markov chain Monte Carlo (MCMC) and varia...
05/10/2019 ∙ by Francisco J. R. Ruiz, et al. ∙ 0 ∙ shareread it

Sparse Orthogonal Variational Inference for Gaussian Processes
We introduce a new interpretation of sparse variational approximations f...
10/23/2019 ∙ by Jiaxin Shi, et al. ∙ 0 ∙ shareread it
Michalis K. Titsias
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Assistant Professor at the Department of Informatics in Athens University of Economics and Business (AUEB).