AutoML-Conf

Goals

The first 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.

Keynote Speakers

Anima Anandkumar
Caltech & NVIDIA
Trinity of Explainable AI: Calibrated, Verifiable, and User-friendly AI
Jeff Clune
University of British Columbia & OpenAI
AI-generating algorithms: the fastest path to AGI?
Chelsea Finn
Stanford University
Meta-Learning for Education
Timnit Gebru
Distributed Artificial Intelligence Research Institute
On the Relationship between Fairness and AutoML (panel discussion)
Julie Josse
INRIA Ecole Polytechnique
Missing data: from inference to imputation and prediction; an overview of the main challenges
Alex Smola
Amazon Web Services
AutoGluon: Recent Advances on AutoML for Tabular Data

Organizers

Frank Hutter
General Chair
University of Freiburg &
Bosch Center for Artificial Intelligence
Mihaela van der Schaar
PC Chair
University of Cambridge
Marius Lindauer
PC Chair
Leibniz University Hannover
Isabelle Guyon
PC Chair
INRIA, University of Paris-Saclay
Raman Arora
Local Chair
Johns Hopkins University
Colin White
Local Chair
Abacus.AI
Joaquin Vanschoren
Tutorial Chair
Eindhoven University of Technology
Alexander Tornede
Review Workflow Chair
University of Paderborn
Theresa Eimer
Diversity Chair
Leibniz University Hannover
Wei-Wei Tu
Wei-Wei Tu
Competition Chair
4Paradigm Inc., China and ChaLearn, USA
Katharina Eggensperger
Social Chair
University of Freiburg
Matthias Feurer
Social Chair
University of Freiburg
Holger Hoos
Journal Track Chair
Leiden University

Senior Area Chairs

Bernd Bischl
Trustworthy AutoML
LMU Munich &
MCML
Debadeepta Dey
NAS
Microsoft Research
Carola Doerr
Evolutionary Computation
CNRS, Sorbonne University
Xuanyi Dong
NAS
Google Brain
Aleksandra Faust
Aleksandra Faust
AutoRL
Google Brain
Roman Garnett
Bayesian Optimization
Washington University in St. Louis
Josif Grabocka
HPO
University of Freiburg
Erin Grant
Meta-Learning
UC Berkeley
Max Jaderberg
Max Jaderberg
AutoRL
DeepMind
Aaron Klein
HPO
AWS Research Berlin
Lars Kotthoff
AutoAI
University of Wyoming
Erin LeDell
Erin LeDell
AutoML Systems
H2O.ai
Hai Li
Hai Li
NAS
Duke University
Hanxiao Liu
NAS
Google Brain
Michael McCourt
Michael McCourt
Bayesian Optimization
SigOpt, an Intel company
Jan Hendrik Metzen
NAS
Bosch Center for Artificial Intelligence
Felix Mohr
Felix Mohr
Tabular AutoML
Universidad de La Sabana
Matthias Poloczek
Bayesian Optimization
Amazon
Esteban Real
Evolutionary AutoML
Google Research / Google Brain Team
Marc Schoenauer
Stochastic and Multi-objective Opt.
INRIA Saclay
Michèle Sebag
Meta-Features
CNRS & Univ. Paris-Saclay
Alex Smola
AutoML Systems
Amazon Web Services
Heike Trautmann
Multi-Obj. Opt. & EAs
University of Münster
Jane Wang
Meta-learning DeepMind
Huaxiu Yao
Meta-learning
Stanford University