Difference between revisions of "ALT 2019"
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{{Event | {{Event | ||
| + | |Acronym=ALT 2019 | ||
| + | |Title=30th International Conference on Algorithmic Learning Theory | ||
| + | |Series=ALT | ||
| + | |Type=Conference | ||
| + | |Start date=2019/03/22 | ||
| + | |End date=2019/03/24 | ||
| + | |Homepage=http://alt2019.algorithmiclearningtheory.org/ | ||
| + | |City=Chicago | ||
| + | |Country=USA | ||
|Has coordinator=Lev Reyzin, Gyorgy Turan | |Has coordinator=Lev Reyzin, Gyorgy Turan | ||
|has program chair=Satyen Kale, Aurélien Garivier | |has program chair=Satyen Kale, Aurélien Garivier | ||
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|Has PC member=Naman Agarwal, Kareem Amin, Borja Balle, Achilles Beros, Gilles Blanchard, Sébastien Bubeck | |Has PC member=Naman Agarwal, Kareem Amin, Borja Balle, Achilles Beros, Gilles Blanchard, Sébastien Bubeck | ||
|has Keynote speaker=Sanjeev Arora, Jennifer Wortman Vaughan | |has Keynote speaker=Sanjeev Arora, Jennifer Wortman Vaughan | ||
| − | | | + | |Submitted papers=78 |
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|Accepted papers=37 | |Accepted papers=37 | ||
| − | | | + | |State=US/IL}} |
== Topics == | == Topics == | ||
* Design and analysis of learning algorithms. | * Design and analysis of learning algorithms. | ||
Revision as of 16:38, 3 November 2021
Event Rating
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List of all ratings can be found at ALT 2019/rating
| ALT 2019 | |
|---|---|
30th International Conference on Algorithmic Learning Theory
| |
| Event in series | ALT |
| Dates | 2019/03/22 (iCal) - 2019/03/24 |
| Homepage: | http://alt2019.algorithmiclearningtheory.org/ |
| Location | |
| Location: | Chicago, US/IL, USA |
| Papers: | Submitted 78 / Accepted 37 (47.4 %) |
| Committees | |
| Organizers: | Lev Reyzin, Gyorgy Turan |
| PC chairs: | Satyen Kale, Aurélien Garivier |
| Workshop chairs: | Steve Hanneke |
| PC members: | Naman Agarwal, Kareem Amin, Borja Balle, Achilles Beros, Gilles Blanchard, Sébastien Bubeck |
| Keynote speaker: | Sanjeev Arora, Jennifer Wortman Vaughan |
| Table of Contents | |
Topics
- Design and analysis of learning algorithms.
- Statistical and computational learning theory.
- Online learning algorithms and theory.
- Optimization methods for learning.
- Unsupervised, semi-supervised, online and active learning.
- Connections of learning with other mathematical fields.
- Artificial neural networks, including deep learning.
- High-dimensional and non-parametric statistics.
- Learning with algebraic or combinatorial structure.
- Bayesian methods in learning.
- Planning and control, including reinforcement learning.
- Learning with system constraints: e.g. privacy, memory or communication budget.
- Learning from complex data: e.g., networks, time series, etc.
- Interactions with statistical physics.
- Learning in other settings: e.g. social, economic, and game-theoretic.