Difference between revisions of "ALT 2020"

From OPENRESEARCH fixed Wiki
Jump to navigation Jump to search
(modified through wikirestore by orapi)
(modified through wikirestore by orapi)
Line 6: Line 6:
 
|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
 
|Acronym=ALT 2020
 
|Acronym=ALT 2020
|End date=2020/02/11
+
|End date=2020-02-11
 
|Series =ALT
 
|Series =ALT
 
|Type  =Conference
 
|Type  =Conference
Line 12: Line 12:
 
|State  =US/CA
 
|State  =US/CA
 
|City  =US/CA/San Diego
 
|City  =US/CA/San Diego
 +
|Year  =2020
 
|Homepage=http://alt2020.algorithmiclearningtheory.org/
 
|Homepage=http://alt2020.algorithmiclearningtheory.org/
 
|Ordinal=31
 
|Ordinal=31
|Start date=2020/02/08
+
|Start date=2020-02-08
 
|Title  =31st International Conference on Algorithmic Learning Theory
 
|Title  =31st International Conference on Algorithmic Learning Theory
 
|Accepted papers=38
 
|Accepted papers=38

Revision as of 03:44, 19 November 2021


Event Rating

median worst
Pain1.svg Pain5.svg

List of all ratings can be found at ALT 2020/rating

ALT 2020
31st International Conference on Algorithmic Learning Theory
Ordinal 31
Event in series ALT
Dates 2020-02-08 (iCal) - 2020-02-11
Homepage: http://alt2020.algorithmiclearningtheory.org/
Location
Location: US/CA/San Diego, US/CA, US
Loading map...

Important dates
Papers: 2019/09/20
Submissions: 2019/09/20
Notification: 2019/11/24
Papers: Submitted 128 / Accepted 38 (29.7 %)
Committees
PC chairs: Aryeh Kontorovich, Gergely Neu
PC members: Yasin Abbasi-Yadkori, Pierre Alquier, Shai Ben-David, Nicolò Cesa-Bianchi, Andrew Cotter, Ilias Diakonikolas
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 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.