(mobo import Concept___Events-migrated) |
(mobo import Concept___Events_With_Metrics-migrated) |
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|has program chair=Aryeh Kontorovich, Gergely Neu | |has program chair=Aryeh Kontorovich, Gergely Neu | ||
|Has PC member=Yasin Abbasi-Yadkori, Pierre Alquier, Shai Ben-David, Nicolò Cesa-Bianchi, Andrew Cotter, Ilias Diakonikolas | |Has PC member=Yasin Abbasi-Yadkori, Pierre Alquier, Shai Ben-David, Nicolò Cesa-Bianchi, Andrew Cotter, Ilias Diakonikolas | ||
| − | |||
| − | |||
|pageCreator=User:Curator 55 | |pageCreator=User:Curator 55 | ||
|pageEditor=User:Curator 27 | |pageEditor=User:Curator 27 | ||
| Line 25: | Line 23: | ||
|Notification Deadline=2019/11/24 | |Notification Deadline=2019/11/24 | ||
|Submission Deadline=2019/09/20 | |Submission Deadline=2019/09/20 | ||
| + | }} | ||
| + | {{Event Metric | ||
| + | |Number Of Submitted Papers=128 | ||
| + | |Number Of Accepted Papers=38 | ||
}} | }} | ||
{{S Event}} | {{S Event}} | ||
Revision as of 13:28, 18 October 2022
Deadlines
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Metrics
Submitted Papers
128
Accepted Papers
38
Venue
San Diego, 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 and active learning.
- Interactive learning, planning and control, and reinforcement 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.
- Learning with system constraints: e.g. privacy, memory or communication budget.
- Learning from complex data: e.g., networks, time series.
- Interactions with statistical physics.
- Learning in other settings: e.g. social, economic, and game-theoretic.
