Difference between revisions of "Event:COLT 2017"

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|Acronym=COLT 2017
 
|Acronym=COLT 2017
 
|Title=30th Annual Conference on Learning Theory
 
|Title=30th Annual Conference on Learning Theory
|Type=Conference
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|Ordinal=30
|Official Website=http://www.learningtheory.org/colt2017/
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|In Event Series=Event Series:COLT
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|Single Day Event=no
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|Start Date=2017/07/07
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|End Date=2017/07/10
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|Event Status=as scheduled
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|Event Mode=on site
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|Venue=UvA Oudemanhuispoort
 
|City=Amsterdam
 
|City=Amsterdam
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|Region=North Holland
 
|Country=Country:NL
 
|Country=Country:NL
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|Academic Field=Computational Learning Theory
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|Official Website=http://www.learningtheory.org/colt2017/
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|Type=Conference
 
|Attendance fee currency=€
 
|Attendance fee currency=€
 
|On site regular=600
 
|On site regular=600
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|pageEditor=User:Curator 72
 
|pageEditor=User:Curator 72
 
|contributionType=1
 
|contributionType=1
|In Event Series=Event Series:COLT
 
|Single Day Event=no
 
|Start Date=2017/07/07
 
|End Date=2017/10/07
 
|Academic Field=Computational Learning Theory
 
|Event Status=as scheduled
 
|Event Mode=on site
 
 
}}
 
}}
 
{{Event Deadline}}
 
{{Event Deadline}}
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{{Organizer
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|Contributor Type=organization
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|Organization=Association for Computational Learning
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{{Event Metric
 
{{Event Metric
 
|Number Of Submitted Papers=228
 
|Number Of Submitted Papers=228

Latest revision as of 09:22, 24 May 2023

Deadlines
organization
Metrics
Submitted Papers
228
Accepted Papers
73
Venue

UvA Oudemanhuispoort, Amsterdam, North Holland, Netherlands

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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

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