ICML 2019 Tutorials

Never Ending Learning

Lecturers: Tom Mitchell and Partha Talukdar

Video: https://www.facebook.com/icml.imls/videos/1083330081864839/

A Primer on PAC-Bayesian Learning

Lecturers: Benjamin Guedj and John Shawe-Taylor

Homepage: https://bguedj.github.io/icml2019/index.html

Keywords: Statistical learning theory, PAC-Bayes, machine learning, computational statistics

Slides are available here.

Videos are available here: Part 1 Part 2

Neural Approaches to Conversational AI

Lecturers: Jianfeng Gao and Michel Galley

Slides: https://icml.cc/media/Slides/icml/2019/grandball(10-09-15)-10-13-00-4342-neural_approach.pdf

Video: https://www.facebook.com/icml.imls/videos/2375117292730871/

Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning

Lecturers: Chelsea Finn and Sergey Levine

Homepage: https://sites.google.com/view/icml19metalearning

Slides: tinyurl.com/icml-meta-slides

Video: https://www.facebook.com/icml.imls/videos/400619163874853/

Active Learning From Theory to Practice

Homepage: http://nowak.ece.wisc.edu/ActiveML.html


Part 1
Part 2
Part 3
Part 4

Video: https://www.facebook.com/icml.imls/videos/662482727539899/

Algorithmic configurations: learning in the space of algorithm designs

Lecturers: Kevin Leyto-Brown and Frank Hutter

Slides: http://ml.informatik.uni-freiburg.de/~hutter/ICML19_AC.pdf

Video: https://www.facebook.com/icml.imls/videos/2044426569187107/

Active Hypothesis Testing: An Information Theoretic (re)View

Lecturer: Tara Javidi

Video: https://www.facebook.com/icml.imls/videos/478549476247044/

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