Difference between revisions of "COLT 2018"

From Openresearch
Jump to: navigation, search
m
 
Line 7: Line 7:
 
|Start date=2018/07/06
 
|Start date=2018/07/06
 
|End date=2018/07/09
 
|End date=2018/07/09
 +
|Submission deadline=2018/02/16
 
|Homepage=https://www.learningtheory.org/colt2018/
 
|Homepage=https://www.learningtheory.org/colt2018/
 
|City=Stockholm
 
|City=Stockholm
|Country=Schweden
+
|Country=Sweden
|Submission deadline=2018/02/16
 
 
|Notification=2018/05/02
 
|Notification=2018/05/02
 
|Submitting link=https://easychair.org/conferences/?conf=colt2018
 
|Submitting link=https://easychair.org/conferences/?conf=colt2018

Latest revision as of 08:33, 1 April 2020

COLT 2018
31st Annual Conference on Learning Theory
Event in series COLT
Dates 2018/07/06 (iCal) - 2018/07/09
Homepage: https://www.learningtheory.org/colt2018/
Submitting link: https://easychair.org/conferences/?conf=colt2018
Location
Location: Stockholm, Sweden
Loading map...

Important dates
Submissions: 2018/02/16
Notification: 2018/05/02
Accepted short papers: 91
Papers: Submitted 335 / Accepted 91 (27.2 %)
Committees
PC chairs: Sebastien Bubeck, Philippe Rigollet
Keynote speaker: Stephane Mallat, Susan Murphy, Johan Hastad
Table of Contents


The following coordinate was not recognized: Geocoding failed.
The following coordinate was not recognized: Geocoding failed.


The 31st Annual Conference on Learning Theory (COLT 2018) will take place in Stockholm, Sweden, on July 5-9, 2018 (with a welcome reception on the 4th), immediately before ICML 2018, which takes place in the same city. We invite submissions of papers addressing theoretical aspects of machine learning and related topics

Topics

  • Design and analysis of learning algorithms
  • Statistical and computational complexity of learning
  • Optimization methods for learning
  • Unsupervised, semi-supervised, online and active learning
  • Interactions with other mathematical fields
  • Interactions with statistical physics
  • Artificial neural networks, including deep learning
  • High-dimensional and non-parametric statistics
  • Learning with algebraic or combinatorial structure
  • Geometric and topological data analysis
  • 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.
  • Learning in other settings: e.g. social, economic, and game-theoretic

Submissions

Important Dates

  • Paper submission deadline: February 16, 2018, 11:00 PM EST
  • Author feedback: April 9-15, 2018
  • Author notification: May 2, 2018
  • Conference: July 6-9, 2018 (welcome reception on the 5th)

Committees

  • Program committee

Jacob Abernethy (Georgia Tech) Shivani Agarwal (University of Pennsylvania) Shipra Agrawal (Columbia University) Alexandr Andoni (Columbia University) Pranjal Awasthi (Rutgers University) Francis Bach (INRIA)

  • Program chairs

Sebastien Bubeck (Microsoft Research) Philippe Rigollet (MIT)

  • Publication chair

Vianney Perchet (ENS Paris-Saclay)

  • Sponsorship Chairs

Satyen Kale (Google) Robert Schapire (Microsoft Research)

  • Local Arrangements chair

Alexandre Proutiere (KTH)