Publications related to High-Dimensional Inference on Dense Graphs with Applications to Coding Theory and Machine Learning

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

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

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

Framework to Approximate the Value of Information of Bridge Load Testing for Reserve Capacity Assessment

Ian Smith, Numa Joy Bertola, Marco Proverbio

In developed countries, structural assessment of existing bridges should not be performed using the same conservative models that are used at the design stage. Field measurements of real behavior provide additional information for the inference of previous ...

2020

Lane Detection and Lane Changing Phenomena Identification with Data from a Swarm of Drones

Nikolaos Geroliminis, Emmanouil Barmpounakis

In the era of big data, new transportation-related concepts and methodologies need to be proposed for understanding how congestion propagates. pNEUMA, a unique dataset that was acquired during a first-of-its-kind experiment with a swarm of drones over a de ...

2020

Common Information Components Analysis

Michael Christoph Gastpar, Erixhen Sula

We give an information-theoretic interpretation of Canonical Correlation Analysis (CCA) via (relaxed) Wyner's common information. CCA permits to extract from two high-dimensional data sets low-dimensional descriptions (features) that capture the commonalit ...

IEEE2020

Phase retrieval in high dimensions: Statistical and computational phase transitions

Florent Gérard Krzakala, Lenka Zdeborová, Bruno Loureiro, Bruno Loureiro

We consider the phase retrieval problem of reconstructing a n -dimensional real or complex signal X ⋆ from m (possibly noisy) observations Y μ = | ∑ n i = 1 Φ μ i X ⋆ i / √ n | , for a large class of correlated real and complex random sensing matrices Φ , ...

Curran Associates, Inc.2020

Converse for Multi-Server Single-Message PIR with Side Information

Michael Christoph Gastpar, Su Li

Multi-server single-message private information retrieval is studied in the presence of side information. In this problem, K independent messages are replicatively stored at N non-colluding servers. The user wants to privately download one message from the ...

2020

EEG microstates are a candidate endophenotype for schizophrenia

Michael Herzog, Christine Mohr, Maya Roinishvili, Ophélie Gladys Favrod, Patricia Figueiredo

Electroencephalogram microstates are recurrent scalp potential configurations that remain stable for around 90 ms. The dynamics of two of the four canonical classes of microstates, commonly labeled as C and D, have been suggested as a potential endophenoty ...

2020

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

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

High-Dimensional Inference on Dense Graphs with Applications to Coding Theory and Machine Learning

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Robust Generalization via $\alpha$-Mutual Information

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

Framework to Approximate the Value of Information of Bridge Load Testing for Reserve Capacity Assessment

Lane Detection and Lane Changing Phenomena Identification with Data from a Swarm of Drones

Common Information Components Analysis

Phase retrieval in high dimensions: Statistical and computational phase transitions

Converse for Multi-Server Single-Message PIR with Side Information

EEG microstates are a candidate endophenotype for schizophrenia

Statistical Physics Methods for Community Detection

Reed-Muller codes polarize

Robust Generalization via $\alpha$-Mutual Information

Lane Detection and Lane Changing Phenomena Identification with Data from a Swarm of Drones

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

Common Information Components Analysis

Phase retrieval in high dimensions: Statistical and computational phase transitions

Framework to Approximate the Value of Information of Bridge Load Testing for Reserve Capacity Assessment

Converse for Multi-Server Single-Message PIR with Side Information

Statistical Physics Methods for Community Detection

EEG microstates are a candidate endophenotype for schizophrenia

Reed-Muller codes polarize