Maurizio Filippone

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University of Glasgow

AXA Chair of Computational Statistics and Assistant Professor at EURECOM

Learning Optimal Conditional Priors For Disentangled Representations

A large part of the literature on learning disentangled representations ...
10/19/2020 ∙ by Graziano Mita, et al. ∙ 0 ∙ share

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Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling

In this work we define a unified mathematical framework to deepen our un...
06/09/2020 ∙ by Giulio Franzese, et al. ∙ 0 ∙ share

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A Variational View on Bootstrap Ensembles as Bayesian Inference

In this paper, we employ variational arguments to establish a connection...
06/08/2020 ∙ by Dimitrios Milios, et al. ∙ 0 ∙ share

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Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting

Accurate travel products price forecasting is a highly desired feature t...
03/16/2020 ∙ by Rosa Candela, et al. ∙ 0 ∙ share

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Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations

Variational inference techniques based on inducing variables provide an ...
03/06/2020 ∙ by Simone Rossi, et al. ∙ 14 ∙ share

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Efficient Approximate Inference with Walsh-Hadamard Variational Inference

Variational inference offers scalable and flexible tools to tackle intra...
11/29/2019 ∙ by Simone Rossi, et al. ∙ 0 ∙ share

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LIBRE: Learning Interpretable Boolean Rule Ensembles

We present a novel method - LIBRE - to learn an interpretable classifier...
11/15/2019 ∙ by Graziano Mita, et al. ∙ 0 ∙ share

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Kernel computations from large-scale random features obtained by Optical Processing Units

Approximating kernel functions with random features (RFs)has been a succ...
10/22/2019 ∙ by Ruben Ohana, et al. ∙ 0 ∙ share

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Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD

Large scale machine learning is increasingly relying on distributed opti...
10/21/2019 ∙ by Rosa Candela, et al. ∙ 0 ∙ share

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Deep Compositional Spatial Models

Nonstationary, anisotropic spatial processes are often used when modelli...
06/06/2019 ∙ by Andrew Zammit Mangion, et al. ∙ 0 ∙ share

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Walsh-Hadamard Variational Inference for Bayesian Deep Learning

Over-parameterized models, such as DeepNets and ConvNets, form a class o...
05/27/2019 ∙ by Simone Rossi, et al. ∙ 0 ∙ share

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A comparative evaluation of novelty detection algorithms for discrete sequences

The identification of anomalies in temporal data is a core component of ...
02/28/2019 ∙ by Rémi Domingues, et al. ∙ 0 ∙ share

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Variational Calibration of Computer Models

Bayesian calibration of black-box computer models offers an established ...
10/29/2018 ∙ by Sébastien Marmin, et al. ∙ 0 ∙ share

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Good Initializations of Variational Bayes for Deep Models

Stochastic variational inference is an established way to carry out appr...
10/18/2018 ∙ by Simone Rossi, et al. ∙ 0 ∙ share

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Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification

In this paper, we study the problem of deriving fast and accurate classi...
05/28/2018 ∙ by Dimitrios Milios, et al. ∙ 6 ∙ share

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Calibrating Deep Convolutional Gaussian Processes

The wide adoption of Convolutional Neural Networks (CNNs) in application...
05/26/2018 ∙ by Gia-Lac Tran, et al. ∙ 0 ∙ share

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Constraining the Dynamics of Deep Probabilistic Models

We introduce a novel generative formulation of deep probabilistic models...
02/15/2018 ∙ by Marco Lorenzi, et al. ∙ 0 ∙ share

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Assessing Bayesian Nonparametric Log-Linear Models: an application to Disclosure Risk estimation

We present a method for identification of models with good predictive pe...
01/16/2018 ∙ by Cinzia Carota, et al. ∙ 0 ∙ share

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Pseudo-extended Markov chain Monte Carlo

Sampling from the posterior distribution using Markov chain Monte Carlo ...
08/17/2017 ∙ by Christopher Nemeth, et al. ∙ 0 ∙ share

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Entropic Trace Estimates for Log Determinants

The scalable calculation of matrix determinants has been a bottleneck to...
04/24/2017 ∙ by Jack Fitzsimons, et al. ∙ 0 ∙ share

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Bayesian Inference of Log Determinants

The log-determinant of a kernel matrix appears in a variety of machine l...
04/05/2017 ∙ by Jack Fitzsimons, et al. ∙ 0 ∙ share

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AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

We investigate the capabilities and limitations of Gaussian process mode...
10/18/2016 ∙ by Karl Krauth, et al. ∙ 0 ∙ share

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Random Feature Expansions for Deep Gaussian Processes

The composition of multiple Gaussian Processes as a Deep Gaussian Proces...
10/14/2016 ∙ by Kurt Cutajar, et al. ∙ 0 ∙ share

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Mini-Batch Spectral Clustering

The cost of computing the spectrum of Laplacian matrices hinders the app...
07/07/2016 ∙ by Yufei Han, et al. ∙ 0 ∙ share

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Preconditioning Kernel Matrices

The computational and storage complexity of kernel machines presents the...
02/22/2016 ∙ by Kurt Cutajar, et al. ∙ 0 ∙ share

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MCMC for Variationally Sparse Gaussian Processes

Gaussian process (GP) models form a core part of probabilistic machine l...
06/12/2015 ∙ by James Hensman, et al. ∙ 0 ∙ share

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Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)

In applications of Gaussian processes where quantification of uncertaint...
01/22/2015 ∙ by Maurizio Filippone, et al. ∙ 0 ∙ share

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Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC

Kernel methods have revolutionized the fields of pattern recognition and...
11/28/2013 ∙ by Maurizio Filippone, et al. ∙ 0 ∙ share

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Pseudo-Marginal Bayesian Inference for Gaussian Processes

The main challenges that arise when adopting Gaussian Process priors in ...
10/02/2013 ∙ by Maurizio Filippone, et al. ∙ 0 ∙ share

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Maurizio Filippone

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