Difference between revisions of "COLT 2017"
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|has Proceedings Link=http://proceedings.mlr.press/v65/ | |has Proceedings Link=http://proceedings.mlr.press/v65/ | ||
|Acronym =COLT 2017 | |Acronym =COLT 2017 | ||
| − | |End date =2017 | + | |End date =2017-10-07 |
|Series =COLT | |Series =COLT | ||
|Type =Conference | |Type =Conference | ||
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|State =NL/NH | |State =NL/NH | ||
|City =NL/NH/Amsterdam | |City =NL/NH/Amsterdam | ||
| + | |Year =2017 | ||
|Homepage =http://www.learningtheory.org/colt2017/ | |Homepage =http://www.learningtheory.org/colt2017/ | ||
| − | |Start date =2017 | + | |Start date =2017-07-07 |
|Title =30th Annual Conference on Learning Theory | |Title =30th Annual Conference on Learning Theory | ||
|Accepted papers=73 | |Accepted papers=73 | ||
| − | |Submitted papers=228}} | + | |Submitted papers=228 |
| + | }} | ||
The 30th Annual Conference on Learning Theory (COLT 2017) will take place in Amsterdam, the Netherlands, on July 7-10, 2017 | The 30th Annual Conference on Learning Theory (COLT 2017) will take place in Amsterdam, the Netherlands, on July 7-10, 2017 | ||
Latest revision as of 04:01, 6 December 2021
Event Rating
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List of all ratings can be found at COLT 2017/rating
| COLT 2017 | |
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30th Annual Conference on Learning Theory
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| Event in series | COLT |
| Dates | 2017-07-07 (iCal) - 2017-10-07 |
| Homepage: | http://www.learningtheory.org/colt2017/ |
| Location | |
| Location: | NL/NH/Amsterdam, NL/NH, NL |
| Early bird student: | € 150 / {{{Early bird fee reduced}}} (reduced) |
| On site student: | € 350 / {{{On site fee reduced}}} (reduced) |
| Early bird regular: | € 250 |
| On site regular: | € 600 |
| Papers: | Submitted 228 / Accepted 73 (32 %) |
| Table of Contents | |
The 30th Annual Conference on Learning Theory (COLT 2017) will take place in Amsterdam, the Netherlands, on July 7-10, 2017
Topics
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
Important Dates
Paper submission deadline: February 17, 2017, 11:00 PM EST Author feedback: April 7-12, 2017 Author notification: May 5, 2017 Conference: July 7-10, 2017 (welcome reception on the 6th)
Committees
- Program Committee
Jake Abernethy (University of Michigan) Alekh Agarwal (Microsoft Research) Shipra Agarwal(Columbia University) Shivani Agarwal (University of Pennsylvania) Anima Anandkumar (University of California Irvine) Peter Auer (Montanuniversitaet Leoben) Pranjal Awasthi (Rutgers
- Program Chairs
Satyen Kale and Ohad Shamir