Difference between revisions of "Event:ALT 2020"

From ConfIDent
(mobo import Concept___Events-migrated)
(mobo import Concept___Events-migrated)
Line 4: Line 4:
 
|Ordinal=31
 
|Ordinal=31
 
|Type=Conference
 
|Type=Conference
|Submission deadline=2019/09/20
 
 
|Homepage=http://alt2020.algorithmiclearningtheory.org/
 
|Homepage=http://alt2020.algorithmiclearningtheory.org/
 
|City=San Diego
 
|City=San Diego
 
|Country=Country:US
 
|Country=Country:US
|Paper deadline=2019/09/20
 
|Notification=2019/11/24
 
 
|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
Line 24: Line 21:
 
|Event Mode=on site
 
|Event Mode=on site
 
}}
 
}}
 +
{{Event Deadline
 +
|Paper Deadline=2019/09/20
 +
|Notification Deadline=2019/11/24
 +
|Submission Deadline=2019/09/20
 +
}}
 +
{{S Event}}
 
== Topics ==
 
== Topics ==
 
* Design and analysis of learning algorithms.
 
* Design and analysis of learning algorithms.

Revision as of 18:38, 22 September 2022

Deadlines
2019-11-24
2019-09-20
2019-09-20
20
Sep
2019
Submission
20
Sep
2019
Paper
24
Nov
2019
Notification
Venue

San Diego, United States of America

Loading map...

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.
Cookies help us deliver our services. By using our services, you agree to our use of cookies.