Also see our YouTube channel!
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
- Warmstarting-friendly leaderboard of the NeurIPS 2020 challenge on blackbox optimization, won by Squirrel
- Slides
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 Biendenkapp 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
- Slides Part 1 (General AutoML, HPO and Meta-Learning, Matthias)
- Slides Part 2 (NAS, Frank)
- Video Part 2 (NAS, Frank) (sorry, in minutes 9-20, the video is stuck, only the audio is fine; from 20:16 onwards, everything is fine again)
Tutorial on Automated Machine Learning at GCPR’19
by Matthias Feurer and Thomas Elsken
- Slides Part 1 (General AutoML, HPO and Meta-Learning, Matthias)
- Slides Part 2 (NAS and Meta-Learning, Thomas)
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
- Slides Parts 1+2 (HPO and NAS, Frank)
- Slides Part 3 (Meta Learning, Joaquin)
- Recordings
- NeurIPS event page
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
- Course Page
- Slides available upon request
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