
All You Need is a Good Functional Prior for Bayesian Deep Learning
The Bayesian treatment of neural networks dictates that a prior distribu...
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Sparse within Sparse Gaussian Processes using Neighbor Information
Approximations to Gaussian processes based on inducing variables, combin...
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Learning Optimal Conditional Priors For Disentangled Representations
A large part of the literature on learning disentangled representations ...
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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...
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A Variational View on Bootstrap Ensembles as Bayesian Inference
In this paper, we employ variational arguments to establish a connection...
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Model Monitoring and Dynamic Model Selection in Travel Timeseries Forecasting
Accurate travel products price forecasting is a highly desired feature t...
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Rethinking Sparse Gaussian Processes: Bayesian Approaches to InducingVariable Approximations
Variational inference techniques based on inducing variables provide an ...
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Efficient Approximate Inference with WalshHadamard Variational Inference
Variational inference offers scalable and flexible tools to tackle intra...
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LIBRE: Learning Interpretable Boolean Rule Ensembles
We present a novel method  LIBRE  to learn an interpretable classifier...
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Kernel computations from largescale random features obtained by Optical Processing Units
Approximating kernel functions with random features (RFs)has been a succ...
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Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD
Large scale machine learning is increasingly relying on distributed opti...
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Deep Compositional Spatial Models
Nonstationary, anisotropic spatial processes are often used when modelli...
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WalshHadamard Variational Inference for Bayesian Deep Learning
Overparameterized models, such as DeepNets and ConvNets, form a class o...
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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 ...
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Variational Calibration of Computer Models
Bayesian calibration of blackbox computer models offers an established ...
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Good Initializations of Variational Bayes for Deep Models
Stochastic variational inference is an established way to carry out appr...
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Dirichletbased Gaussian Processes for Largescale Calibrated Classification
In this paper, we study the problem of deriving fast and accurate classi...
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Calibrating Deep Convolutional Gaussian Processes
The wide adoption of Convolutional Neural Networks (CNNs) in application...
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Constraining the Dynamics of Deep Probabilistic Models
We introduce a novel generative formulation of deep probabilistic models...
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Assessing Bayesian Nonparametric LogLinear Models: an application to Disclosure Risk estimation
We present a method for identification of models with good predictive pe...
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Pseudoextended Markov chain Monte Carlo
Sampling from the posterior distribution using Markov chain Monte Carlo ...
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Entropic Trace Estimates for Log Determinants
The scalable calculation of matrix determinants has been a bottleneck to...
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Bayesian Inference of Log Determinants
The logdeterminant of a kernel matrix appears in a variety of machine l...
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AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
We investigate the capabilities and limitations of Gaussian process mode...
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Random Feature Expansions for Deep Gaussian Processes
The composition of multiple Gaussian Processes as a Deep Gaussian Proces...
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MiniBatch Spectral Clustering
The cost of computing the spectrum of Laplacian matrices hinders the app...
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Preconditioning Kernel Matrices
The computational and storage complexity of kernel machines presents the...
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MCMC for Variationally Sparse Gaussian Processes
Gaussian process (GP) models form a core part of probabilistic machine l...
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Enabling scalable stochastic gradientbased inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
In applications of Gaussian processes where quantification of uncertaint...
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Bayesian Inference for Gaussian Process Classifiers with Annealing and PseudoMarginal MCMC
Kernel methods have revolutionized the fields of pattern recognition and...
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PseudoMarginal Bayesian Inference for Gaussian Processes
The main challenges that arise when adopting Gaussian Process priors in ...
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Maurizio Filippone
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AXA Chair of Computational Statistics and Assistant Professor at EURECOM