(mobo import Concept___Events-migrated) |
(mobo import Concept___Events_With_Metrics-migrated) |
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|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 | ||
| − | |||
| − | |||
|pageCreator=User:Curator 55 | |pageCreator=User:Curator 55 | ||
|pageEditor=User:Curator 55 | |pageEditor=User:Curator 55 | ||
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}} | }} | ||
{{Event Deadline}} | {{Event Deadline}} | ||
| + | {{Event Metric | ||
| + | |Number Of Submitted Papers=78 | ||
| + | |Number Of Accepted Papers=37 | ||
| + | }} | ||
{{S Event}} | {{S Event}} | ||
== Topics == | == Topics == | ||
Revision as of 13:28, 18 October 2022
The document "ALT 2019" was published on "2022-11-01T08:53:43" on the website "ConfIDent" under the URL https://confident-conference.org/index.php/Event:ALT 2019.
The document "ALT 2019" describes an event in the sense of a conference.
The document "ALT 2019" contains information about the event "ALT 2019" with start date "2019/03/22" and end date "2019/03/24".
The event "ALT 2019" is part of the event series identified by [[Event Series:ALT]]
Deadlines
Metrics
Submitted Papers
78
Accepted Papers
37
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.
