Publications associées à Mutual Information for the Stochastic Block Model by the Adaptive Interpolation Method

Impact of phylogeny on structural contact inference from protein sequence data

Nicola Dietler, Umberto Lupo, Anne-Florence Raphaëlle Bitbol

Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ance ...

ROYAL SOC2023

A Functional Perspective on Information Measures

Amedeo Roberto Esposito

Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Other ...

EPFL2022

Mutual information in changing environments: Nonlinear interactions, out-of-equilibrium systems, and continuously varying diffusivities

Daniel Maria Busiello, Giorgio Nicoletti

Biochemistry, ecology, and neuroscience are examples of prominent fields aiming at describing interacting systems that exhibit nontrivial couplings to complex, ever-changing environments. We have recently shown that linear interactions and a switching envi ...

AMER PHYSICAL SOC2022

The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

Michael Christoph Gastpar, Erixhen Sula

This paper presents explicit solutions for two related non-convex information extremization problems due to Gray and Wyner in the Gaussian case. The first problem is the Gray-Wyner network subject to a sum-rate constraint on the two private links. Here, ou ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Michael Christoph Gastpar, Adrien Vandenbroucque, Amedeo Roberto Esposito

We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of f-Divergences. The results are then applied to specific settings of interest and compared to other notable results i ...

2022

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Nicola Dietler, Anne-Florence Raphaëlle Bitbol, Andonis Samuel Rene Gerardos

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families ...

Public Library Science2022

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method

Nicolas Macris, Jean François Emmanuel Barbier

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...

AMER PHYSICAL SOC2022

Mutual Information Disentangles Interactions from Changing Environments

Daniel Maria Busiello, Giorgio Nicoletti

Real-world systems are characterized by complex interactions of their internal degrees of freedom, while living in ever-changing environments whose net effect is to act as additional couplings. Here, we introduce a paradigmatic interacting model in a switc ...

AMER PHYSICAL SOC2021

A Comparison of optimal measurement-system design with engineering judgement for bridge load testing

Ian Smith, Numa Joy Bertola

Due to conservative approaches in construction design and practice, infrastructure often has hidden reserve capacity. When quantified, this reserve has potential to improve decisions related to asset management. Field measurements, collected through load t ...

2021

Mutual information for low-rank even-order symmetric tensor estimation

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 asymptotic mutual information when the tensor is of even order. The proof applies the adaptive interpolation method orig ...

OXFORD UNIV PRESS2021

Common Information Components Analysis

Michael Christoph Gastpar, Erixhen Sula

Wyner's common information is a measure that quantifies and assesses the commonality between two random variables. Based on this, we introduce a novel two-step procedure to construct features from data, referred to as Common Information Components Analysis ...

2021

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Erixhen Sula

Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular network ...

EPFL2021

Low-Cost Side-Channel Secure Standard 6T-SRAM-Based Memory With a 1% Area and Less Than 5% Latency and Power Overheads

Robert Giterman

Side-channel attacks constitute a concrete threat to IoT systems-on-a-chip (SoCs). Embedded memories implemented with 6T SRAM macrocells often dominate the area and power consumption of these SoCs. Regardless of the computational platform, the side-channel ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Statistical limits of high-dimensional inference problems

Clément Dominique Luneau

This thesis focuses on two kinds of statistical inference problems in signal processing and data science. The first problem is the estimation of a structured informative tensor from the observation of a noisy tensor in which it is buried. The structure com ...

EPFL2021

A methodology to design measurement systems when multiple model classes are plausible

Ian Smith, Numa Joy Bertola, Sai Ganesh Sarvotham Pai

The management of existing civil infrastructure is challenging due to evolving functional requirements, aging and climate change. Civil infrastructure often has hidden reserve capacity because of conservative approaches used in design and during constructi ...

2021

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation

Nicolas Macris, Mohamad Baker Dia, Jean François Emmanuel Barbier, Florent Gérard Krzakala

We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projections. A few examples where this problem is relevant are compressed sensing, sparse superposition codes, and code division multiple access. There has been a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

Strengthened Information-theoretic Bounds on the Generalization Error

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

The following problem is considered: given a joint distribution P XY and an event E, bound P XY (E) in terms of P X P Y (E) (where P X P Y is the product of the marginals of P XY ) and a measure of dependence of X and Y. Such bounds have direct application ...

IEEE2019