AutoML 2023

Goals

The international conference on automated machine learning (AutoML) is the premier gathering of professionals focussed on the progressive automation of machine learning (ML), aiming to develop automated methods for making ML methods more efficient, robust, trustworthy, and available to everyone. A special focus of the AutoML conference lies on openness: via sharing of code, we hope to facilitate a culture of open collaborations across academic and industrial partners.

Keynotes

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Mihaela van der Schaar

University of Cambridge

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Eytan Bakshy

Meta

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Hayeon Lee

KAIST

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Esteban Real

Google Research

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Madeleine Udell

Stanford University

General and Program Chairs

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Frank

Hutter

General Chair

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Aleksandra

Faust

Program Chair

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Roman

Garnett

Program Chair

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Colin

White

Program Chair

Additional Chairs

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Jacob

Gardner

Publications Chair

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Joaquin

Vanschoren

Systems, Benchmarks
& Challenges Chair

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Aaron

Klein

Local Chair

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Ralf

Herbrich

Local Chair

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Yingjie

Miao

Review Workflow Chair

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Katharina

Eggensperger

Social Chair

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Matthias

Feurer

Social Chair

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Lennart

Purucker

Reproducibility Chair

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Alexander

Tornede

Tutorial Chair

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Vincent

Dumoulin

Journal Track Chair

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Sam Ade

Jacobs

Journal Track Chair

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Marius

Lindauer

Publicity Chair

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Katarzyna

Woźnica

Website Chair

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Anna

Kozak

Website Chair

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Tejaswini

Pedapati

Diversity Chair

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Wenming

Ye

Sponsorship Chair

All organizers at a glance

Venue

Credits by HPI / Kay Herschelmann, HPI / Lutz Hannemann