Cédric Archambeau
Amazon.com
121 Papers
889 Citations
Cédric Archambeau is an academic researcher from Amazon.com. The author has contributed to research in topics: Computer science & Bayesian optimization. The author has an hindex of 30, co-authored 106 publications. Previous affiliations of Cédric Archambeau include University College London & Université catholique de Louvain.
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Papers
The variational gaussian approximation revisited
Manfred Opper,Cédric Archambeau +1 more
TL;DR: The relationship between the Laplace and the variational approximation is discussed, and it is shown that for models with gaussian priors and factorizing likelihoods, the number of variational parameters is actually .
464
Template attacks in principal subspaces
Cédric Archambeau,E. Peeters,François-Xavier Standaert,Jean-Jacques Quisquater +3 more
- 10 Oct 2006
TL;DR: This work proposes to perform template attacks in the principal subspace of the traces, a new type of attack that requires five time less encrypted messages than the best reported correlation attack against similar block cipher implementations.
Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages
François-Xavier Standaert,Cédric Archambeau +1 more
- 10 Aug 2008
TL;DR: It is shown how classical statistical tools such as Principal Component Analysis and Fisher Linear Discriminant Analysis can be used for efficiently preprocessing the leakage traces and evaluates the effectiveness of two data dimensionality reduction techniques for constructing subspace-based template attacks.
•Posted Content
LEEP: A New Measure to Evaluate Transferability of Learned Representations
TL;DR: This paper proposed the Log Expected Empirical Prediction (LEEP) measure to evaluate the transferability of representations learned by classifiers, which is simple and easy to compute: when given a classifier trained on a source data set, it only requires running the target data set through this classifier once.
178
•Proceedings Article
Sparse probabilistic projections
Cédric Archambeau,Francis Bach +1 more
- 08 Dec 2008
TL;DR: This work presents a generative model for performing sparse probabilistic projections, which includes sparse principal component analysis and sparse canonical correlation analysis as special cases, and derives a variational Expectation-Maximisation algorithm for the estimation of the hyperparameters.