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|Homepage=https://www.learningtheory.org/colt2018/ | |Homepage=https://www.learningtheory.org/colt2018/ | ||
|City=Stockholm | |City=Stockholm | ||
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|Notification=2018/05/02 | |Notification=2018/05/02 | ||
|Submitting link=https://easychair.org/conferences/?conf=colt2018 | |Submitting link=https://easychair.org/conferences/?conf=colt2018 | ||
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|pageEditor=User:Curator 89 | |pageEditor=User:Curator 89 | ||
|contributionType=1 | |contributionType=1 | ||
+ | |Event Status=as scheduled | ||
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The 31st Annual Conference on Learning Theory (COLT 2018) will take place in Stockholm, Sweden, on July 5-9, 2018 (with a welcome reception on the 4th), immediately before ICML 2018, which takes place in the same city. We invite submissions of papers addressing theoretical aspects of machine learning and related topics | The 31st Annual Conference on Learning Theory (COLT 2018) will take place in Stockholm, Sweden, on July 5-9, 2018 (with a welcome reception on the 4th), immediately before ICML 2018, which takes place in the same city. We invite submissions of papers addressing theoretical aspects of machine learning and related topics |
Revision as of 13:53, 6 September 2022
The 31st Annual Conference on Learning Theory (COLT 2018) will take place in Stockholm, Sweden, on July 5-9, 2018 (with a welcome reception on the 4th), immediately before ICML 2018, which takes place in the same city. We invite submissions of papers addressing theoretical aspects of machine learning and related topics
Topics
- Design and analysis of learning algorithms
- Statistical and computational complexity of learning
- Optimization methods for learning
- Unsupervised, semi-supervised, online and active learning
- Interactions with other mathematical fields
- Interactions with statistical physics
- Artificial neural networks, including deep learning
- High-dimensional and non-parametric statistics
- Learning with algebraic or combinatorial structure
- Geometric and topological data analysis
- Bayesian methods in learning
- Planning and control, including reinforcement learning
- Learning with system constraints: e.g. privacy, memory or communication budget
- Learning from complex data: e.g., networks, time series, etc.
- Learning in other settings: e.g. social, economic, and game-theoretic
Submissions
Important Dates
- Paper submission deadline: February 16, 2018, 11:00 PM EST
- Author feedback: April 9-15, 2018
- Author notification: May 2, 2018
- Conference: July 6-9, 2018 (welcome reception on the 5th)
Committees
- Program committee
Jacob Abernethy (Georgia Tech) Shivani Agarwal (University of Pennsylvania) Shipra Agrawal (Columbia University) Alexandr Andoni (Columbia University) Pranjal Awasthi (Rutgers University) Francis Bach (INRIA)
- Program chairs
Sebastien Bubeck (Microsoft Research) Philippe Rigollet (MIT)
- Publication chair
Vianney Perchet (ENS Paris-Saclay)
- Sponsorship Chairs
Satyen Kale (Google) Robert Schapire (Microsoft Research)
- Local Arrangements chair
Alexandre Proutiere (KTH)