COLT 2019

COLT 2019
32nd Annual Conference on Learning Theory
Event in series	COLT
Subevent of	ACM Federated Computing Research Conference
Dates	2019/06/25 (iCal) - 2019/06/28
Homepage:	http://learningtheory.org/colt2019/
Submitting link:	https://easychair.org/account/signin?l=lOLq96R6Naa07cDcCkvZ45
Location
Location:	Phoenix, Arizona, USA
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Important dates
Papers:	2019/02/01
Submissions:	2019/05/10
Notification:	2019/05/24
Registration link:	https://www.cvent.com/events/fcrc-2019/registration-78e7bfed5fc9437291908ea8f0950311.aspx?fqp=true
Early bird student:	USD 250,00 ",00" can not be assigned to a declared number type with value 250. / {{{Early bird fee reduced}}} Property "Early bird fee reduced" (as page type) with input value "{{{Early bird fee reduced}}}" contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process. (reduced)
On site student:	USD 375,00 ",00" can not be assigned to a declared number type with value 375. / {{{On site fee reduced}}} Property "On site fee reduced" (as page type) with input value "{{{On site fee reduced}}}" contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process. (reduced)
Early bird regular:	USD 425,00 ",00" can not be assigned to a declared number type with value 425.
Committees
Organizers:	Omer Ben-Porat;, Nika Haghtalab;, Yishay Mansour;, Tim Roughgarden;, Association for Computational Learning;
PC chairs:	Alina Beygelzimer;, Daniel Hsu;
Table of Contents

The 32nd Annual Conference on Learning Theory (COLT 2019) will take place in Phoenix, Arizona, June 25-28, 2019, as part of the ACM Federated Computing Research Conference, which also includes EC and STOC

Topics

• Design and analysis of learning algorithms
• Statistical and computational complexity of learning
• Optimization methods for learning
• Unsupervised and semi-supervised learning
• Interactive learning, planning and control, and reinforcement learning
• Online learning and decision-making under uncertainty
• Interactions of learning theory 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
• Game theory and learning
• Learning with system constraints (e.g., privacy, computational, memory, communication)
• Learning from complex data: e.g., networks, time series
• Learning in other settings: e.g., computational social science, economics

Submissions

Submissions by authors who are new to COLT are encouraged. While the primary focus of the conference is theoretical, the authors may support their analysis by including relevant experimental results. All accepted papers will be presented in a single track at the conference. At least one of each paper’s authors should be present at the conference to present the work. Accepted papers will be published electronically in the Proceedings of Machine Learning Research (PMLR). The authors of accepted papers will have the option of opting-out of the proceedings in favor of a 1-page extended abstract. The full paper reviewed for COLT will then be placed on the arXiv repository.

Important Dates

• Submission Deadline February 1
• Author Feedback March 22-27
• Authors Notification April 17
• Early Registration Ends May 24

Committees

• Program chairs:
  • Alina Beygelzimer (Yahoo! Research)
  • Daniel Hsu (Columbia University)
• Sponsorship chairs
  • Satyen Kale (Google)
  • Robert Schapire (Microsoft Research)
• Local Arrangements Chairs
  • Yishay Mansour (Tel Aviv University and Google)
  • Peter Grunwald (Centrum Wiskunde & Informatica)