23 citations · 58 across the 10 of their papers we have counts for
10 papers
Entropic Mirror Monte Carlo
Anas Cherradi, Yazid Janati, Alain Durmus +3
Importance sampling is a Monte Carlo method which designs estimators of expectations under a target distribution using weighted samples from a proposal distribution. When the targe…
When to Transfer: Adaptive Source Selection for Positive Transfer in Linear Models
Hamza Cherkaoui, Hélène Halconruy, Yohan Petetin
In many business settings, task-specific labeled data are scarce or costly to obtain, limiting supervised learning on a target task. A classical response is transfer learning (TL).…
A Probabilistic Semi-Supervised Approach with Triplet Markov Chains
Katherine Morales, Yohan Petetin
Triplet Markov chains are general generative models for sequential data which take into account three kinds of random variables: (noisy) observations, their associated discrete lab…
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpoint
François Desbouvries, Yohan Petetin, Achille Salaün
Hidden Markov Chains (HMC) and Recurrent Neural Networks (RNN) are two well known tools for predicting time series. Even though these solutions were developed independently in dist…
Variance estimation for Sequential Monte Carlo Algorithms: a backward sampling approach
Yazid Janati El idrissi, Sylvain Le Corff, Yohan Petetin
In this paper, we consider the problem of online asymptotic variance estimation for particle filtering and smoothing. Current solutions for the particle filter rely on the particle…
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics
Nicolas Aussel, Sophie Chabridon, Yohan Petetin
Machine Learning has proven useful in the recent years as a way to achieve failure prediction for industrial systems. However, the high computational resources necessary to run lea…