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{{Event | {{Event | ||
|Acronym=ALT 2019 | |Acronym=ALT 2019 | ||
| − | |Title= | + | |Title=International Conference on Algorithmic Learning Theory |
| − | | | + | |Ordinal=30 |
| − | | | + | |In Event Series=Event Series:ALT |
| + | |Single Day Event=no | ||
| + | |Start Date=2019/03/22 | ||
| + | |End Date=2019/03/24 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=on site | ||
|City=Chicago | |City=Chicago | ||
|Country=Country:US | |Country=Country:US | ||
| + | |Official Website=http://alt2019.algorithmiclearningtheory.org/ | ||
| + | |Type=Conference | ||
|Has coordinator=Lev Reyzin, Gyorgy Turan | |Has coordinator=Lev Reyzin, Gyorgy Turan | ||
|has program chair=Satyen Kale, Aurélien Garivier | |has program chair=Satyen Kale, Aurélien Garivier | ||
|has workshop chair=Steve Hanneke | |has workshop chair=Steve Hanneke | ||
|Has PC member=Naman Agarwal, Kareem Amin, Borja Balle, Achilles Beros, Gilles Blanchard, Sébastien Bubeck | |Has PC member=Naman Agarwal, Kareem Amin, Borja Balle, Achilles Beros, Gilles Blanchard, Sébastien Bubeck | ||
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|pageCreator=User:Curator 55 | |pageCreator=User:Curator 55 | ||
|pageEditor=User:Curator 55 | |pageEditor=User:Curator 55 | ||
|contributionType=1 | |contributionType=1 | ||
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}} | }} | ||
| − | == Topics == | + | {{Event Deadline}} |
| − | * Design and analysis of learning algorithms. | + | {{Event Metric |
| − | * Statistical and computational learning theory. | + | |Number Of Submitted Papers=78 |
| − | * Online learning algorithms and theory. | + | |Number Of Accepted Papers=37 |
| − | * Optimization methods for learning. | + | }} |
| − | * Unsupervised, semi-supervised, online and active learning. | + | {{S Event}} |
| − | * Connections of learning with other mathematical fields. | + | ==Topics== |
| − | * Artificial neural networks, including deep learning. | + | *Design and analysis of learning algorithms. |
| − | * High-dimensional and non-parametric statistics. | + | *Statistical and computational learning theory. |
| − | * Learning with algebraic or combinatorial structure. | + | *Online learning algorithms and theory. |
| − | * Bayesian methods in learning. | + | *Optimization methods for learning. |
| − | * Planning and control, including reinforcement learning. | + | *Unsupervised, semi-supervised, online and active learning. |
| − | * Learning with system constraints: e.g. privacy, memory or communication budget. | + | *Connections of learning with other mathematical fields. |
| − | * Learning from complex data: e.g., networks, time series, etc. | + | *Artificial neural networks, including deep learning. |
| − | * Interactions with statistical physics. | + | *High-dimensional and non-parametric statistics. |
| − | * Learning in other settings: e.g. social, economic, and game-theoretic. | + | *Learning with algebraic or combinatorial structure. |
| + | *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. | ||
| + | *Interactions with statistical physics. | ||
| + | *Learning in other settings: e.g. social, economic, and game-theoretic. | ||
Latest revision as of 08:53, 1 November 2022
Deadlines
Metrics
Submitted Papers
78
Accepted Papers
37
Venue
Chicago, 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, online and active 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.
- 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.
- Interactions with statistical physics.
- Learning in other settings: e.g. social, economic, and game-theoretic.
