Maurizio Filippone

research

∙ 06/30/2021

Revisiting the Effects of Stochasticity for Hamiltonian Samplers

We revisit the theoretical properties of Hamiltonian stochastic differen...

Giulio Franzese, et al. ∙

research

∙ 06/11/2021

Model Selection for Bayesian Autoencoders

We develop a novel method for carrying out model selection for Bayesian ...

Ba-Hien Tran, et al. ∙

research

∙ 11/25/2020

All You Need is a Good Functional Prior for Bayesian Deep Learning

The Bayesian treatment of neural networks dictates that a prior distribu...

Ba-Hien Tran, et al. ∙

research

∙ 11/10/2020

Sparse within Sparse Gaussian Processes using Neighbor Information

Approximations to Gaussian processes based on inducing variables, combin...

Gia-Lac Tran, et al. ∙

research

∙ 10/19/2020

Learning Optimal Conditional Priors For Disentangled Representations

A large part of the literature on learning disentangled representations ...

Graziano Mita, et al. ∙

research

∙ 06/09/2020

Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling

In this work we define a unified mathematical framework to deepen our un...

Giulio Franzese, et al. ∙

research

∙ 06/08/2020

A Variational View on Bootstrap Ensembles as Bayesian Inference

In this paper, we employ variational arguments to establish a connection...

Dimitrios Milios, et al. ∙

research

∙ 03/16/2020

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

Accurate travel products price forecasting is a highly desired feature t...

Rosa Candela, et al. ∙

research

∙ 03/06/2020

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

Variational inference techniques based on inducing variables provide an ...

Simone Rossi, et al. ∙

research

∙ 11/29/2019

Efficient Approximate Inference with Walsh-Hadamard Variational Inference

Variational inference offers scalable and flexible tools to tackle intra...

Simone Rossi, et al. ∙

research

∙ 11/15/2019

LIBRE: Learning Interpretable Boolean Rule Ensembles

We present a novel method - LIBRE - to learn an interpretable classifier...

Graziano Mita, et al. ∙

research

∙ 10/22/2019

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

Approximating kernel functions with random features (RFs)has been a succ...

Ruben Ohana, et al. ∙

research

∙ 10/21/2019

Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD

Large scale machine learning is increasingly relying on distributed opti...

Rosa Candela, et al. ∙

research

∙ 06/06/2019

Deep Compositional Spatial Models

Nonstationary, anisotropic spatial processes are often used when modelli...

Andrew Zammit Mangion, et al. ∙

research

∙ 05/27/2019

Walsh-Hadamard Variational Inference for Bayesian Deep Learning

Over-parameterized models, such as DeepNets and ConvNets, form a class o...

Simone Rossi, et al. ∙

research

∙ 02/28/2019

A comparative evaluation of novelty detection algorithms for discrete sequences

The identification of anomalies in temporal data is a core component of ...

Rémi Domingues, et al. ∙

research

∙ 10/29/2018

Variational Calibration of Computer Models

Bayesian calibration of black-box computer models offers an established ...

Sébastien Marmin, et al. ∙

research

∙ 10/18/2018

Good Initializations of Variational Bayes for Deep Models

Stochastic variational inference is an established way to carry out appr...

Simone Rossi, et al. ∙

research

∙ 05/28/2018

Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification

In this paper, we study the problem of deriving fast and accurate classi...

Dimitrios Milios, et al. ∙

research

∙ 05/26/2018

Calibrating Deep Convolutional Gaussian Processes

The wide adoption of Convolutional Neural Networks (CNNs) in application...

Gia-Lac Tran, et al. ∙

research

∙ 02/15/2018

Constraining the Dynamics of Deep Probabilistic Models

We introduce a novel generative formulation of deep probabilistic models...

Marco Lorenzi, et al. ∙

research

∙ 01/16/2018

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

We present a method for identification of models with good predictive pe...

Cinzia Carota, et al. ∙

research

∙ 08/17/2017

Pseudo-extended Markov chain Monte Carlo

Sampling from the posterior distribution using Markov chain Monte Carlo ...

Christopher Nemeth, et al. ∙

research

∙ 04/24/2017

Entropic Trace Estimates for Log Determinants

The scalable calculation of matrix determinants has been a bottleneck to...

Jack Fitzsimons, et al. ∙

research

∙ 04/05/2017

Bayesian Inference of Log Determinants

The log-determinant of a kernel matrix appears in a variety of machine l...

Jack Fitzsimons, et al. ∙

research

∙ 10/18/2016

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

We investigate the capabilities and limitations of Gaussian process mode...

Karl Krauth, et al. ∙

research

∙ 10/14/2016

Random Feature Expansions for Deep Gaussian Processes

The composition of multiple Gaussian Processes as a Deep Gaussian Proces...

Kurt Cutajar, et al. ∙

research

∙ 07/07/2016

Mini-Batch Spectral Clustering

The cost of computing the spectrum of Laplacian matrices hinders the app...

Yufei Han, et al. ∙

research

∙ 02/22/2016

Preconditioning Kernel Matrices

The computational and storage complexity of kernel machines presents the...

Kurt Cutajar, et al. ∙

research

∙ 06/12/2015

MCMC for Variationally Sparse Gaussian Processes

Gaussian process (GP) models form a core part of probabilistic machine l...

James Hensman, et al. ∙

research

∙ 01/22/2015

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...

Maurizio Filippone, et al. ∙

research

∙ 11/28/2013

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

Kernel methods have revolutionized the fields of pattern recognition and...

Maurizio Filippone, et al. ∙

research

∙ 10/02/2013

Pseudo-Marginal Bayesian Inference for Gaussian Processes

The main challenges that arise when adopting Gaussian Process priors in ...

Maurizio Filippone, et al. ∙

Maurizio Filippone

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