Difference between revisions of "ALT 2020"
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{{Event | {{Event | ||
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|Submission deadline=2019/09/20 | |Submission deadline=2019/09/20 | ||
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|Paper deadline=2019/09/20 | |Paper deadline=2019/09/20 | ||
|Notification=2019/11/24 | |Notification=2019/11/24 | ||
|has program chair=Aryeh Kontorovich, Gergely Neu | |has program chair=Aryeh Kontorovich, Gergely Neu | ||
|Has PC member=Yasin Abbasi-Yadkori, Pierre Alquier, Shai Ben-David, Nicolò Cesa-Bianchi, Andrew Cotter, Ilias Diakonikolas | |Has PC member=Yasin Abbasi-Yadkori, Pierre Alquier, Shai Ben-David, Nicolò Cesa-Bianchi, Andrew Cotter, Ilias Diakonikolas | ||
| + | |Acronym=ALT 2020 | ||
| + | |End date=2020-02-11 | ||
| + | |Series =ALT | ||
| + | |Type =Conference | ||
| + | |Country=US | ||
| + | |State =US/CA | ||
| + | |City =US/CA/San Diego | ||
| + | |Year =2020 | ||
| + | |Homepage=http://alt2020.algorithmiclearningtheory.org/ | ||
| + | |Ordinal=31 | ||
| + | |Start date=2020-02-08 | ||
| + | |Title =31st International Conference on Algorithmic Learning Theory | ||
| + | |Accepted papers=38 | ||
|Submitted papers=128 | |Submitted papers=128 | ||
| − | + | }} | |
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== Topics == | == Topics == | ||
* Design and analysis of learning algorithms. | * Design and analysis of learning algorithms. | ||
Latest revision as of 04:21, 6 December 2021
Event Rating
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List of all ratings can be found at ALT 2020/rating
| ALT 2020 | |
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31st International Conference on Algorithmic Learning Theory
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| Ordinal | 31 |
| Event in series | ALT |
| Dates | 2020-02-08 (iCal) - 2020-02-11 |
| Homepage: | http://alt2020.algorithmiclearningtheory.org/ |
| Location | |
| Location: | US/CA/San Diego, US/CA, US |
| Important dates | |
| Papers: | 2019/09/20 |
| Submissions: | 2019/09/20 |
| Notification: | 2019/11/24 |
| Papers: | Submitted 128 / Accepted 38 (29.7 %) |
| Committees | |
| PC chairs: | Aryeh Kontorovich, Gergely Neu |
| PC members: | Yasin Abbasi-Yadkori, Pierre Alquier, Shai Ben-David, Nicolò Cesa-Bianchi, Andrew Cotter, Ilias Diakonikolas |
| Table of Contents | |
Topics
- Design and analysis of learning algorithms.
- Statistical and computational learning theory.
- Online learning algorithms and theory.
- Optimization methods for learning.
- Unsupervised, semi-supervised and active learning.
- Interactive learning, planning and control, and reinforcement learning.
- Connections of learning 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.
- Learning with system constraints: e.g. privacy, memory or communication budget.
- Learning from complex data: e.g., networks, time series.
- Interactions with statistical physics.
- Learning in other settings: e.g. social, economic, and game-theoretic.