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|Acronym=COLT 2019 | |Acronym=COLT 2019 | ||
|Title=32nd Annual Conference on Learning Theory | |Title=32nd Annual Conference on Learning Theory | ||
| + | |In Event Series=Event Series:COLT | ||
| + | |Single Day Event=no | ||
| + | |Start Date=2019/06/25 | ||
| + | |End Date=2019/06/28 | ||
| + | |Academic Field=Machine Learning | ||
|Type=Conference | |Type=Conference | ||
|Superevent=ACM Federated Computing Research Conference | |Superevent=ACM Federated Computing Research Conference | ||
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The 32nd Annual Conference on Learning Theory (COLT 2019) will take place in Phoenix, Arizona, June 25-28, 2019, as part of the ACM Federated Computing Research Conference, which also includes EC and STOC | The 32nd Annual Conference on Learning Theory (COLT 2019) will take place in Phoenix, Arizona, June 25-28, 2019, as part of the ACM Federated Computing Research Conference, which also includes EC and STOC | ||
| Line 60: | Line 60: | ||
*Early Registration Ends May 24 | *Early Registration Ends May 24 | ||
==Committees== | ==Committees== | ||
| − | * Program chairs: | + | *Program chairs: |
**Alina Beygelzimer (Yahoo! Research) | **Alina Beygelzimer (Yahoo! Research) | ||
**Daniel Hsu (Columbia University) | **Daniel Hsu (Columbia University) | ||
Revision as of 09:05, 31 August 2022
The 32nd Annual Conference on Learning Theory (COLT 2019) will take place in Phoenix, Arizona, June 25-28, 2019, as part of the ACM Federated Computing Research Conference, which also includes EC and STOC
Topics
- Design and analysis of learning algorithms
- Statistical and computational complexity of learning
- Optimization methods for learning
- Unsupervised and semi-supervised learning
- Interactive learning, planning and control, and reinforcement learning
- Online learning and decision-making under uncertainty
- Interactions of learning theory with other mathematical fields
- Artificial neural networks, including deep learning
- High-dimensional and non-parametric statistics
- Learning with algebraic or combinatorial structure
- Bayesian methods in learning
- Game theory and learning
- Learning with system constraints (e.g., privacy, computational, memory, communication)
- Learning from complex data: e.g., networks, time series
- Learning in other settings: e.g., computational social science, economics
Submissions
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 Proceedings of Machine Learning Research (PMLR). 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.
Important Dates
- Submission Deadline February 1
- Author Feedback March 22-27
- Authors Notification April 17
- Early Registration Ends May 24
Committees
- Program chairs:
- Alina Beygelzimer (Yahoo! Research)
- Daniel Hsu (Columbia University)
- Sponsorship chairs
- Satyen Kale (Google)
- Robert Schapire (Microsoft Research)
- Local Arrangements Chairs
- Yishay Mansour (Tel Aviv University and Google)
- Peter Grunwald (Centrum Wiskunde & Informatica)
- Keynote Speakers
- Emma Brunskill (Stanford)
- Moritz Hardt (Berkeley)
