ALT 2020: Difference between revisions
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|Acronym=ALT 2020 | |Acronym=ALT 2020 | ||
|Title=31st International Conference on Algorithmic Learning Theory | |Title=31st International Conference on Algorithmic Learning Theory | ||
|Ordinal=31 | |||
|Series=ALT | |Series=ALT | ||
|Type=Conference | |Type=Conference | ||
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|Accepted papers=38 | |Accepted papers=38 | ||
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== 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. | |||
Latest revision as of 13:01, 4 March 2021
| ALT 2020 | |
|---|---|
31st International Conference on Algorithmic Learning Theory
| |
| Ordinal | 31 |
| Event in series | ALT |
| Dates | 2020/02/08 (iCal) - 2020/02/11 |
| Homepage: | http://alt2020.algorithmiclearningtheory.org/ |
| Location | |
| Location: | San Diego, USA |
| 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.