Robust algorithms for learning from modern data

Projektinformationen

SEQUOIA

ID Finanzhilfevereinbarung: 724063

DOI 10.3030/724063

Abgeschlossenes Projekt

EK-Unterschriftsdatum 16 März 2017

Startdatum 1 September 2017

Enddatum 31 August 2023

Finanziert unter

• EXCELLENT SCIENCE - European Research Council (ERC)

Gesamtkosten € 1 998 750,00

EU-Beitrag € 1 998 750,00

Koordiniert durch

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE
France

Ziel

Machine learning is needed and used everywhere, from science to industry, with a growing impact on many disciplines. While first successes were due at least in part to simple supervised learning algorithms used primarily as black boxes on medium-scale problems, modern data pose new challenges. Scalability is an important issue of course: with large amounts of data, many current problems far exceed the capabilities of existing algorithms despite sophisticated computing architectures. But beyond this, the core classical model of supervised machine learning, with the usual assumptions of independent and identically distributed data, or well-defined features, outputs and loss functions, has reached its theoretical and practical limits.

Given this new setting, existing optimization-based algorithms are not adapted. The main objective of this proposal is to push the frontiers of supervised machine learning, in terms of (a) scalability to data with massive numbers of observations, features, and tasks, (b) adaptability to modern computing environments, in particular for parallel and distributed processing, (c) provable adaptivity and robustness to problem and hardware specifications, and (d) robustness to non-convexities inherent in machine learning problems.

To achieve the expected breakthroughs, we will design a novel generation of learning algorithms amenable to a tight convergence analysis with realistic assumptions and efficient implementations. They will help transition machine learning algorithms towards the same wide-spread robust use as numerical linear algebra libraries. Outcomes of the research described in this proposal will include algorithms that come with strong convergence guarantees and are well-tested on real-life benchmarks coming from computer vision, bioinformatics, audio processing and natural language processing. For both distributed and non-distributed settings, we will release open-source software, adapted to widely available computing platforms.

Wissenschaftliches Gebiet

• natural sciences › computer and information sciences › artificial intelligence › machine learning › supervised learning
• natural sciences › mathematics › pure mathematics › algebra › linear algebra
• natural sciences › computer and information sciences › software
• natural sciences › computer and information sciences › data science › natural language processing
• natural sciences › computer and information sciences › artificial intelligence › computer vision

Programm/Programme

• H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme

Thema/Themen

• ERC-2016-COG - ERC Consolidator Grant

Aufforderung zur Vorschlagseinreichung

ERC-2016-COG

Finanzierungsplan

ERC-COG - Consolidator Grant

Gastgebende Einrichtung

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE

• Netto-EU-Beitrag: € 1 998 750,00
• Adresse: DOMAINE DE VOLUCEAU ROCQUENCOURT ; 78153 Le Chesnay Cedex ; Frankreich
• Region: Ile-de-France › Ile-de-France › Yvelines
• Aktivitätstyp: Research Organisations
• Gesamtkosten: € 1 998 750,00

Begünstigte (1)

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE

Frankreich

• Netto-EU-Beitrag: € 1 998 750,00
• Adresse: DOMAINE DE VOLUCEAU ROCQUENCOURT ; 78153 Le Chesnay Cedex
• Region: Ile-de-France › Ile-de-France › Yvelines
• Aktivitätstyp: Research Organisations
• Gesamtkosten: € 1 998 750,00