AutoML.org

Freiburg-Hannover-Tübingen

Tutorials and Invited Talks

Also see our YouTube channel!

Lecture Series at the Euro Phd School on Data Science Meets Combinatorial Optimization (DSO)

by Alexander Tornede and Marius Lindauer

Lecture at Summer School for Responsible AI at Leibniz University Hannover

by Marius Lindauer

Test of Time Award Presentation at KDD 2023:
“A Decade of AutoML: Reflections and the Road Ahead“

by Frank Hutter

  • The award was given for the KDD 2013 paper “Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms” by Chris Thornton, Frank Hutter, Holger Hoos and Kevin Leyton-Brown
  • Slides

Lecture Series at European Summer School on AI (ESSAI): “AutoML: Accelerating Research on and Development of AI Applications“

by Katharina Eggensperger and Marius Lindauer

Keynote at CVPR 2023 NAS Workshop:
Extending the Versatile Workhorse of Blackbox NAS

by Frank Hutter

TabPFN: SOTA AutoML in 1 Second? at COSEAL’23

by Frank Hutter

Human-Centered AutoML at COSEAL’23

by Marius Lindauer

Invited Talk (fachfremde Mittagsvorlesung) at Freiburger Knorpeltage: Deep Larning 2.0: AI that Builds AI

by Frank Hutter

Invited Talk at ML and Data Science Groups of University Oldenburg: AutoML: Replacing Data Scientists?

by Marius Lindauer

Invited Talk at the Seminar on Advances in Probabilistic Machine Learning: Learning to Dynamically Optimise Algorithms

by André Biedenkapp

Lecture at AutoML Fall School 2022: The AutoML Landscape

by Marius Lindauer and Frank Hutter

Keynote at CVPR NAS workshop 2022: NAS benchmarks: Past, Present and Future

by Frank Hutter

Invited Talk at MINDS CDT Seminar, Southhampton, 2022: Deep Learning 2.0: Extending the Power of Deep Learning to the Meta-Level

by Frank Hutter

Invited Talk at Google BayesOpt Speaker Series 2022: Deep Learning 2.0: How Bayesian Optimization May Power the Next Generation of DL

by Frank Hutter

Invited Talk at TAILOR Seminar 2022: Next Generation Deep Learning for State-of-the-Art Performance on Tabular Data

by Frank Hutter

Keynote at Meta-Learning Competition at NeurIPS 2021: DL 2.0: How Meta-Learning May Power the Next Generation of Deep Learning

by Frank Hutter

Keynote at Toronto Machine Learning Summit 2021: AutoML: Towards Deep Learning 2.0

by Frank Hutter

Invited Talk at SecondMind: Towards Deep Learning 2.0: Going to the Meta-Level

by Frank Hutter

Invited Talk at JAII (Paderborn & Bielefeld, Germany): Efficient and Explainable AutoML

by Marius Lindauer

Introductory lecture at AutoML Fall School 2021: Neural Architecture Search: an Overview

by Frank Hutter

Introductory lecture at AutoML Fall School 2021: AutoML: an Overview

by Bernd Bischl and Marius Lindauer

Invited Talk at Samsung Research and ICCV 2021 Neural Architects workshop:
NAS Benchmarks: Past, Present and Future

by Frank Hutter

Invited Talk at ODSC Europe: Evolution of Efficient and Robust AutoML Systems

by Frank Hutter

Invited Talk at ELLIS AutoML Seminar: Towards Explainable AutoML: xAutoML

by Marius Lindauer

Invited Talk at ELLIS AutoML Seminar: Neural Ensemble Search for Uncertainty Estimation and Dataset Shift

by Arber Zela

Invited Talk at the PUNCHLunch NFDI: AutoML 101

by Marius Lindauer

Talk at the Applied AI Summit 2021: AutoML — AI that builds AI

by Frank Hutter

Podcast Interview about AutoML at “TechTiefen” (German)

by Marius Lindauer

Talk at NeurIPS 2020 Competition Blackbox Optimization for Machine Learning: Squirrel: a Switching Hyperparameter Optimizer

by Frank Hutter

Invited Talk at NeurIPS 2020 Workshop on MetaLearning: Meta-Learning Neural Architectures, Initial Weights, Hyperparameters, and Algorithm Components

by Frank Hutter

Invited Talk at Lorentz Center Workshop 2020: Algorithm configuration, selection + friends: from static to dynamic algorithm configuration

by Frank Hutter

Invited Talk at ICAPS 2020: Getting the most out of your planner(s): from static to dynamic algorithm configuration

by Frank Hutter

Invited Talk in MSR Speaker Series on Directions in ML: Neural Architecture Search: Coming Of Age

by Frank Hutter

Invited Talk at PPSN workshop on Understanding Machine Learning and Optimization Problems (UMLOP): Towards Understanding Automated Deep Learning

by Marius Lindauer

Invited Talk in ELLIS AutoML seminar: Neural Architecture Search: Coming of Age

by Frank Hutter

Keynote at KDD 2020 workshop on AutoML: Towards Robust & Efficient AutoML

by Frank Hutter

L3S Invited Talk: Evolution of AutoML: From Static to Dynamic

by Marius Lindauer

PPSN’20/IJCAI’20 Tutorial: Algorithm Configuration: Challenges, Methods and Perspectives

by André Biedenkapp and Marius Lindauer

Invited Talk at NeurIPS Workshop on Challenges in Machine Learning: A Proposal for a New Competition Design Emphasizing Scientific Insight

by Frank Hutter

ECML’19 Summer School: Lectures + Hands-on about AutoML

by Matthias Feurer and Frank Hutter

Tutorial on Automated Machine Learning at GCPR’19

by Matthias Feurer and Thomas Elsken

Tutorial on Meta-Learning at CVPR’19

by N. Naik, N. Keskar, C. Finn, F. Hutter, R. Socher, R. Raskar

Algorithm configuration: learning in the space of algorithm designs at ICML’19

by Kevin Leyton-Brown and Frank Hutter

Algorithm selection key-note talk and hands-on AutoML at AMIR’19

by Marius Lindauer

AutoML tutorial at NeurIPS 2018

by Frank Hutter and Joaquin Vanschoren

AutoML tutorial at ODSC 2018 in London

by Marius Lindauer

AutoML tutorial at ECML 2017

by Joaquin Vanschoren, Pavel Brazdil, Holger Hoos and Frank Hutter

Towards true end-to-end learning & optimization, Invited Talk at ECML-PKDD’17

by Frank Hutter

Automatic SAT Solver configuration with SMAC, Tutorial at SAT Industrial Day 2016

by Marius Lindauer

Algorithm Configuration: How to boost the performance of your SAT solver? Tutorial at SAT Summer School 2016

by Marius Lindauer

Machine Learning and Optimization for Algorithm Design, Winter Term’15

by Marius Lindauer, Mustafa Misir and Frank Hutter

Algorithm Configuration — A Hands on Tutorial, Tutorial at AAAI’16

by Frank Hutter and Marius Lindauer

In this hands-on tutorial, we will demonstrate how to effectively use algorithm con guration in practice. Attendees do not require any specialized knowledge and will walk away with hands-on experience in con figuring various algorithms that will allow them to apply algorithm con guration in their respective fields of research.

Programming by Optimization: A Practical Paradigm for Computer-Aided Algorithm Design, Tutorial at AAAI’14

by Holger Hoos, Frank Hutter and Kevin Leyton-Brown

Programming by Optimization: A Practical Paradigm for Computer-Aided Algorithm Design, Tutorital at IJCA’13

by Holger Hoos, Frank Hutter and Kevin Leyton-Brown

Algorithm Selection and Configuration, Tutorial at AAAI’13

by Frank Hutter, Lars Kotthoff, Yuri Malitsky, Barry O’Sullivan, and Lin Xu