(mobo import Concept___Events-migrated) |
(mobo import Concept___Events-migrated) |
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| Line 10: | Line 10: | ||
|has workshop chair=Steve Hanneke | |has workshop chair=Steve Hanneke | ||
|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 | ||
| − | |||
|Submitted papers=78 | |Submitted papers=78 | ||
|Accepted papers=37 | |Accepted papers=37 | ||
| Line 23: | Line 22: | ||
|Event Mode=on site | |Event Mode=on site | ||
}} | }} | ||
| + | {{Event Deadline}} | ||
| + | {{S Event}} | ||
== Topics == | == Topics == | ||
* Design and analysis of learning algorithms. | * Design and analysis of learning algorithms. | ||
Revision as of 18:37, 22 September 2022
Deadlines
Venue
Chicago, United States of America
Warning: Venue is missing. The map might not show the exact location.
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.
