Conference on Learning Theory (COLT 2016)
When : 2016-06-23
Where : USA
Submission Deadline : 2016-02-12
Categories : Machine Learning , Artificial Intelligence
Conference on Learning Theory (COLT 2016)
June 23-26, 2016
USA
Call For Papers
The conference will be co-located with ICML (held on June 19-24) and will feature plenary talks by David Donoho (Stanford), Ravi Kannan (Microsoft Research) and Ronitt Rubinfeld (MIT and Tel Aviv University).
All accepted papers will be presented in a single track at the conference, either as a longer (20 minutes) or a shorter (10 minute) talk. 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 JMLR Workshop and Conference Proceedings series. The authors of accepted papers will have the option of opting-out of the proceedings in favor of an extended abstract. The full paper reviewed for COLT will then be placed on the arXiv repository.
Topics
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Design and analysis of learning algorithms
Statistical and computational complexity of learning
Optimization models and algorithms for learning
Unsupervised, semi-supervised, and active learning
Online learning
Artificial neural networks, including deep learning
Learning with large-scale datasets
Decision making under uncertainty
Bayesian methods in learning
High dimensional and non-parametric statistical inference
Planning and control, including reinforcement learning
Learning with additional constraints: e.g. privacy, memory or communication budget
Learning in other settings: e.g. social, economic, and game-theoretic
Analysis and applications of learning theory in related fields: natural language processing, neuroscience, bioinformatics, privacy and security, machine vision, information retrieval
Submissions by authors who are new to COLT are encouraged. While the primary focus of the conference is theoretical, the authors are encouraged to support their analysis by including relevant experimental results.