no code implementations • 20 Oct 2023 • Eric Aubinais, Elisabeth Gassiat, Pablo Piantanida
Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensitive information about individuals.
no code implementations • 21 Sep 2023 • Elisabeth Gassiat, Ibrahim Kaddouri, Zacharie Naulet
The aim of this work is to study the Bayes risk of clustering when using HMMs and to propose associated clustering procedures.
no code implementations • 24 Jun 2021 • Kweku Abraham, Zacharie Naulet, Elisabeth Gassiat
We study the frontier between learnable and unlearnable hidden Markov models (HMMs).
1 code implementation • NeurIPS 2021 • Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvarinen
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA).
1 code implementation • 16 Feb 2021 • Jean Ollion, Charles Ollion, Elisabeth Gassiat, Luc Lehéricy, Sylvain Le Corff
Assuming that the noisy observations are independent conditionally to the signal, the networks can be jointly trained without clean training data.
no code implementations • 11 Jan 2021 • Kweku Abraham, Ismael Castillo, Elisabeth Gassiat
Given a nonparametric Hidden Markov Model (HMM) with two states, the question of constructing efficient multiple testing procedures is considered, treating one of the states as an unknown null hypothesis.
Statistics Theory Statistics Theory 62G10 (primary), 62M05 (secondary)
no code implementations • 12 Jan 2018 • Mor Absa Loum, Marie-Anne Poursat, Abdourahmane Sow, Amadou Sall, Cheikh Loucoubar, Elisabeth Gassiat
Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases.