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The Variational Gaussian Approximation Revisited
TLDR
In this letter, we discuss the relationship between the Laplace and the variational approximation, and we show that for models with gaussian priors and factorizing likelihoods, the number of variational parameters is actually . Expand
Sparse probabilistic projections
TLDR
We present a generative model for performing sparse probabilistic projections, which includes sparse principal component analysis and sparse canonical correlation analysis as special cases. Expand
Template Attacks in Principal Subspaces
TLDR
We propose a probabilistic side-channel attack in the principal subspace of the traces. Expand
Robust probabilistic projections
TLDR
We introduce robust probabilistic principal component analysis (PPCA) and robust canonical correlation analysis. Expand
Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages
TLDR
We investigate the common belief that electromagnetic leakages lead to more powerful attacks than their power consumption counterpart. Expand
Gaussian Process Approximations of Stochastic Differential Equations
TLDR
We present a novel Gaussian process approximation to the posterior measure over paths for a general class of stochastic differential equations in the presence of observations. Expand
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
TLDR
We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints , which are constraints that need to be satisfied when accumulated over a finite number of rounds T . Expand
Robust Bayesian Matrix Factorisation
TLDR
We analyse the noise arising in collaborative filtering when formalised as a probabilistic matrix factorisation problem. Expand
Robust Bayesian clustering
TLDR
We formalize the Bayesian Student-t mixture model as a latent variable model in a different way from Svensén and Bishop. Expand
Variational Inference for Diffusion Processes
TLDR
We propose a variational approach to the approximate inference of stochastic differential equations from a finite set of noisy observations. Expand
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