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{{Event | {{Event | ||
|Acronym=ALT 2020 | |Acronym=ALT 2020 | ||
| − | |Title= | + | |Title=International Conference on Algorithmic Learning Theory |
|Ordinal=31 | |Ordinal=31 | ||
| − | | | + | |In Event Series=Event Series:ALT |
| − | | | + | |Single Day Event=no |
| + | |Start Date=2020/02/08 | ||
| + | |End Date=2020/02/11 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=on site | ||
|City=San Diego | |City=San Diego | ||
|Country=Country:US | |Country=Country:US | ||
| + | |Official Website=http://alt2020.algorithmiclearningtheory.org/ | ||
| + | |Type=Conference | ||
|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 | ||
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|pageEditor=User:Curator 27 | |pageEditor=User:Curator 27 | ||
|contributionType=1 | |contributionType=1 | ||
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}} | }} | ||
{{Event Deadline | {{Event Deadline | ||
| + | |Submission Deadline=2019/09/20 | ||
| + | |Notification Deadline=2019/11/24 | ||
|Paper Deadline=2019/09/20 | |Paper Deadline=2019/09/20 | ||
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}} | }} | ||
{{Event Metric | {{Event Metric | ||
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}} | }} | ||
{{S Event}} | {{S Event}} | ||
| − | == Topics == | + | ==Topics== |
| − | * Design and analysis of learning algorithms. | + | *Design and analysis of learning algorithms. |
| − | * Statistical and computational learning theory. | + | *Statistical and computational learning theory. |
| − | * Online learning algorithms and theory. | + | *Online learning algorithms and theory. |
| − | * Optimization methods for learning. | + | *Optimization methods for learning. |
| − | * Unsupervised, semi-supervised and active learning. | + | *Unsupervised, semi-supervised and active learning. |
| − | * Interactive learning, planning and control, and reinforcement learning. | + | *Interactive learning, planning and control, and reinforcement learning. |
| − | * Connections of learning with other mathematical fields. | + | *Connections of learning with other mathematical fields. |
| − | * Artificial neural networks, including deep learning. | + | *Artificial neural networks, including deep learning. |
| − | * High-dimensional and non-parametric statistics. | + | *High-dimensional and non-parametric statistics. |
| − | * Learning with algebraic or combinatorial structure. | + | *Learning with algebraic or combinatorial structure. |
| − | * Bayesian methods in learning. | + | *Bayesian methods in learning. |
| − | * Learning with system constraints: e.g. privacy, memory or communication budget. | + | *Learning with system constraints: e.g. privacy, memory or communication budget. |
| − | * Learning from complex data: e.g., networks, time series. | + | *Learning from complex data: e.g., networks, time series. |
| − | * Interactions with statistical physics. | + | *Interactions with statistical physics. |
| − | * Learning in other settings: e.g. social, economic, and game-theoretic. | + | *Learning in other settings: e.g. social, economic, and game-theoretic. |
Latest revision as of 08:53, 1 November 2022
Deadlines
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Metrics
Submitted Papers
128
Accepted Papers
38
Venue
San Diego, United States of America
Warning: Venue is missing. The map might not show the exact location.
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.
