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SO-ReCoDi SIGNED

Spectral and Optimization Techniques for Robust Recovery, Combinatorial Constructions, and Distributed Algorithms

Total Cost €

0

EC-Contrib. €

0

Partnership

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 SO-ReCoDi project word cloud

Explore the words cloud of the SO-ReCoDi project. It provides you a very rough idea of what is the project "SO-ReCoDi" about.

context    sensor    mss    algorithms    constructions    lightweight    natural    srivastava    kadison    ways    mentioned    optimization    proof    singer    machine    kinds    contain    faults    regularity    contains    deep    science    technically    matrix    learning    computer    motivated    szemeredi    combinatorial    perturbations    deal    networks    tools    construction    unifying    data    occurring    population    structure    conjecture    graphs    adversarial    sparsifiers    naturally    community    lemma    mix    exist    translate    series    computed    compressed    ing    marcus    convex    recovery    spectral    idea    technique    models    distributed    representations    domains    model    underpinning    largely    certain    robust    chances    speed    theoretical    noise    programming    protocols    shows    unsupervised    theory    random    led    made    explicit    unification    computing    breakthrough    dynamics    semidefinite    unexpected    pursuing    recovering    detection    conceptually    goals    spielman    connection   

Project "SO-ReCoDi" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITA COMMERCIALE LUIGI BOCCONI 

Organization address
address: VIA SARFATTI 25
city: MILANO
postcode: 20136
website: www.unibocconi.it

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Total cost 1˙971˙805 €
 EC max contribution 1˙971˙805 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-ADG
 Funding Scheme ERC-ADG
 Starting year 2019
 Duration (year-month-day) from 2019-09-01   to  2024-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITA COMMERCIALE LUIGI BOCCONI IT (MILANO) coordinator 1˙971˙805.00

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 Project objective

In a recovery problem, we are interested in recovering structure from data that contains a mix of combinatorial structure and random noise. In a robust recovery problem, the data may contain adversarial perturbations as well. A series of recent results in theoretical computer science has led to algorithms based on the convex optimization technique of Semidefinite Programming for several recovery problems motivated by unsupervised machine learning. Can those algorithms be made robust? Sparsifiers are compressed representations of graphs that speed up certain algorithms. The recent proof of the Kadison-Singer conjecture by Marcus, Spielman and Srivastava (MSS) shows that certain kinds of sparsifiers exist, but the proof does not provide an explicit construction. Dynamics and population protocols are simple models of distributed computing that were introduced to study sensor networks and other lightweight distributed systems, and have also been used to model naturally occurring networks. What can and cannot be computed in such models is largely open. We propose an ambitious unifying approach to go beyond the state of the art in these three domains, and provide: robust recovery algorithms for the problems mentioned above; a new connection between sparsifiers and the Szemeredi Regularity Lemma and explicit constructions of the sparsifiers resulting from the MSS work; and an understanding of the ability of simple distributed algorithms to solve community detection problems and to deal with noise and faults. The unification is provided by a common underpinning of spectral methods, random matrix theory, and convex optimization. Such tools are used in technically similar but conceptually very different ways in the three domains. By pursuing these goals together, we will make it more likely that an idea that is natural and simple in one context will translate to an idea that is deep and unexpected in another, increasing the chances of a breakthrough.

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The information about "SO-RECODI" are provided by the European Opendata Portal: CORDIS opendata.

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