
Sequential Changepoint Detection in Neural Networks with Checkpoints
We introduce a framework for online changepoint detection and simultaneo...
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Unbiased Gradient Estimation for Variational AutoEncoders using Coupled Markov Chains
The variational autoencoder (VAE) is a deep latent variable model that ...
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Information Theoretic Meta Learning with Gaussian Processes
We formulate meta learning using information theoretic concepts such as ...
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Gradientbased Adaptive Markov Chain Monte Carlo
We introduce a gradientbased learning method to automatically adapt Mar...
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Sparse Orthogonal Variational Inference for Gaussian Processes
We introduce a new interpretation of sparse variational approximations f...
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Prescribed Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful approach to unsupe...
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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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Functional Regularisation for Continual Learning using Gaussian Processes
We introduce a novel approach for supervised continual learning based on...
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Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules
We propose a probabilistic framework to directly insert prior knowledge ...
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Unbiased Implicit Variational Inference
We develop unbiased implicit variational inference (UIVI), a method that...
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Fully Scalable Gaussian Processes using Subspace Inducing Inputs
We introduce fully scalable Gaussian processes, an implementation scheme...
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Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in c...
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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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Rejectionfree 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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Bayesian Boolean Matrix Factorisation
Boolean matrix factorisation aims to decompose a binary data matrix into...
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Auxiliary gradientbased sampling algorithms
We introduce a new family of MCMC samplers that combine auxiliary variab...
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The Generalized Reparameterization Gradient
The reparameterization gradient has become a widely used method to obtai...
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OnevsEach Approximation to Softmax for Scalable Estimation of Probabilities
The softmax representation of probabilities for categorical variables pl...
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Overdispersed BlackBox Variational Inference
We introduce overdispersed blackbox variational inference, a method to ...
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Inference for determinantal point processes without spectral knowledge
Determinantal point processes (DPPs) are point process models that natur...
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Local Expectation Gradients for Doubly Stochastic Variational Inference
We introduce local expectation gradients which is a general purpose stoc...
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Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
The Gaussian process latent variable model (GPLVM) provides a flexible ...
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Variational Gaussian Process Dynamical Systems
High dimensional time series are endemic in applications of machine lear...
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Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes
Interest in multioutput kernel methods is increasing, whether under the ...
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Michalis K. Titsias
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Assistant Professor at the Department of Informatics in Athens University of Economics and Business (AUEB).