Publication

Mutual Information Disentangles Interactions from Changing Environments

Publications associées (31)

Mutual Information for the Stochastic Block Model by the Adaptive Interpolation Method

Nicolas Macris, Chun Lam Chan, Jean François Emmanuel Barbier

We rigorously derive a single-letter variational expression for the mutual information of the asymmetric two-groups stochastic block model in the dense graph regime. Existing proofs in the literature are indirect, as they involve mapping the model to a ran ...

IEEE2019

Entropy and mutual information in models of deep neural networks

Nicolas Macris, Jean François Emmanuel Barbier, Clément Dominique Luneau, Florent Gérard Krzakala, Lenka Zdeborová

We examine a class of stochastic deep learning models with a tractable method to compute information-theoretic quantities. Our contributions are three-fold: (i) we show how entropies and mutual informations can be derived from heuristic statistical physics ...

2019

Subadditivity Beyond Trees and the Chi-Squared Mutual Information

Emmanuel Abbé, Enric Boix Adsera

Evans et al. [1] proved the subadditivity of the mutual information in the broadcasting on tree model with binary vertex labels and symmetric edge channels. They raised the question of whether such subadditivity extends to loopy graphs in some appropriate ...

IEEE2019

Statistical Physics Methods for Community Detection

Chun Lam Chan

This thesis is devoted to information-theoretic aspects of community detection. The importance of community detection is due to the massive amount of scientific data today that describes relationships between items from a network, e.g., a social network. I ...

EPFL2019

The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference

Nicolas Macris, Jean François Emmanuel Barbier

In recent years important progress has been achieved towards proving the validity of the replica predictions for the (asymptotic) mutual information (or free energy) in Bayesian inference problems. The proof techniques that have emerged appear to be quite ...

SPRINGER HEIDELBERG2019

Theoretical foundations of forward feature selection methods based on mutual information

Francisco Santos Paredes Quartin de Macedo

Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various ...

ELSEVIER SCIENCE BV2019

Mutual Information for Low-Rank Even-Order Symmetric Tensor Factorization

Nicolas Macris, Jean François Emmanuel Barbier, Clément Dominique Luneau

We consider a statistical model for finite-rank symmetric tensor factorization and prove a single-letter variational expression for its mutual information when the tensor is of even order. The proof uses the adaptive interpolation method, for which rank-on ...

IEEE2019

Measuring transient phase-amplitude coupling using local mutual information

Here we demonstrate the suitability of a local mutual information measure for estimating the temporal dynamics of cross-frequency coupling (CFC) in brain electrophysiological signals. In CFC, concurrent activity streams in different frequency ranges intera ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

Quantifying high-order interdependencies via multivariate extensions of the mutual information

Michael Christoph Gastpar, Fernando Rosas de Andraca

This paper introduces a model-agnostic approach to study statistical synergy, a form of emergence in which patterns at large scales are not traceable from lower scales. Our framework leverages various multivariate extensions of Shannon's mutual information ...

2019

Reed-Muller codes polarize

Emmanuel Abbé, Min Ye

Reed-Muller (RM) codes were introduced in 1954 and have long been conjectured to achieve Shannon's capacity on symmetric channels. The activity on this conjecture has recently been revived with the emergence of polar codes. RM codes and polar codes are gen ...

IEEE COMPUTER SOC2019

Optimal errors and phase transitions in high-dimensional generalized linear models

Nicolas Macris, Jean François Emmanuel Barbier, Florent Gérard Krzakala, Lenka Zdeborová

Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing, error-corre ...

National Academy of Sciences2019