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
Caltech & NVIDIA
Trinity of Explainable AI: Calibrated, Verifiable, and User-friendly AI
University of British Columbia & OpenAI
AI-generating algorithms: the fastest path to AGI?
Distributed Artificial Intelligence Research Institute
On the Relationship between Fairness and AutoML (panel discussion)
INRIA Ecole Polytechnique
Missing data: from inference to imputation and prediction; an overview of the main challenges
Organizers

Wei-Wei Tu
Competition Chair
4Paradigm Inc., China and ChaLearn, USA
Senior Area Chairs

Aleksandra Faust
AutoRL
Google Brain

Max Jaderberg
AutoRL
DeepMind

Erin LeDell
AutoML Systems
H2O.ai

Hai Li
NAS
Duke University

Michael McCourt
Bayesian Optimization
SigOpt, an Intel company

Felix Mohr
Tabular AutoML
Universidad de La Sabana