活动简介
The 30th Annual Conference on Learning Theory (COLT 2017) will take place in Amsterdam, the Netherlands, on July 7-10, 2017 (with a welcome reception and ManfredFest on the 6th)
We invite submissions of papers addressing theoretical aspects of machine learning and related topics.
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 JMLR Workshop and Conference Proceedings series. 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.
征稿信息
重要日期
征稿范围
We strongly support a broad definition of learning theory, including, but not limited to:
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Design and analysis of learning algorithms
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Statistical and computational complexity of learning
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Optimization models and algorithms for learning
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Unsupervised, semi-supervised, and active learning
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Online learning
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Artificial neural networks, including deep learning
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Learning with large-scale datasets
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Decision making under uncertainty
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Bayesian methods in learning
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High dimensional and non-parametric statistical inference
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Planning and control, including reinforcement learning
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Learning with additional constraints: e.g. privacy, memory or communication budget
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Learning in other settings: e.g. social, economic, and game-theoretic
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Analysis and applications of learning theory in related fields: natural language processing, neuroscience, bioinformatics, privacy and security, machine vision,
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information retrieval
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重要日期
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会议日期
07月07日
2017
至07月10日
2017
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02月17日 2017
初稿截稿日期
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07月10日 2017
注册截止日期
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