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2024
Talk at University of British Columbia
Highly Accurate Predictions on Small Data with the Tabular Foundation Model TabPFN
by Frank Hutter [Slides]
Talk at edaforum, “Faster, Lager, More Sustainable?
by Marius Lindauer, [Slides]
Talk at KONKIS, 1st conference of the German AI Service Centers
by Marius Lindauer [Slides]
Invited Talk, WiMLDS Paris X AutoML Conference: A Short Story on AutoML
by Katharina Eggensperger [Website][Slides]
Invited Talk, 6th Annual ML in Science Conference: Automated Machine Learning for Science
by Katharina Eggensperger [Website]
Invited Talk, Women in Data Science Regensburg: AutoML: Streamlining Machine Learning
by Katharina Eggensperger [Website] [Slides]
Talk at COSEAL24: Towards Multi-Objective Green AutoML
by Marius Lindauer
ECAI’24 Tutorial: Beyond Trial & Error: A Tutorial on Automated Reinforcement Learning
by Theresa Eimer and André Biedenkapp
Invited talk at the RWTH Aachen (AIM Group): From Full Automation to A Human-Centric Approach
by Marius Lindauer
Invited talk at the University of Bremen (CS/BIPS) on “AutoML: From Full Automation to A Human-Centric Approach”
by Marius Lindauer
2023
Talks at AutoML Fall School 2023
by Carolin Benjamins, Alexander Tornede and Marius Lindauer
- AutoML in the Age of Large Language Models
- Hands-On: Experiment Configuration with Hydra
- Hands-On: Practical Hyperparameter Optimization with SMAC
Talk at MLOps 2023: Hyperparameter Optimieren mit AutoML
by Marius Lindauer and Katharina Eggensperger
Invited talk at Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI): Deep Learning 2.0: Towards AI that Builds AI
by Frank Hutter
Keynote at Brazilian Conference on Intelligent Systems (BRACIS): Deep Learning 2.0: Towards AI that Builds AI
by Frank Hutter
Invited Talk about “From Predictions to Sustainability: Rethinking AutoML Priorities” at AI Double Feature: Neuro-Symbolic AI / AI and Sustainability at LMU (Munich)
by Marius Lindauer
Lecture Series at the Euro Phd School on Data Science Meets Combinatorial Optimization (DSO)
by Alexander Tornede and Marius Lindauer
- Session 1: Algorithm Selection
- Session 2: Algorithm Configuration
- Session 3: Hands-On SMAC
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
- Day I: General Introduction to AutoML
- Day II: Bayesian Optimization for Hyperparameter Optimization
- Day III: Neural Architecture Search
- Day IV: Human-Centered AutoML
- Day V: AutoML Systems and Lookout
Keynote at CVPR 2023 NAS Workshop: Extending the Versatile Workhorse of Blackbox NAS
by Frank Hutter
Hands-On Session at the nextgen_AI Freiburg workshop: Automated Machine Learning with Auto-sklearn
By Katharina Eggensperger and Eddie Bergman
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 Learning 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
2022
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
2021
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
2020
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 Biedenkapp and Marius Lindauer
2019
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
2018 and before
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