(mobo import Concept___Event_For_Confident-migrated) |
(mobo import Concept___Events-migrated) |
||
| Line 7: | Line 7: | ||
|Homepage=http://alt2020.algorithmiclearningtheory.org/ | |Homepage=http://alt2020.algorithmiclearningtheory.org/ | ||
|City=San Diego | |City=San Diego | ||
| − | |Country= | + | |Country=Country:US |
|Paper deadline=2019/09/20 | |Paper deadline=2019/09/20 | ||
|Notification=2019/11/24 | |Notification=2019/11/24 | ||
| Line 21: | Line 21: | ||
|Start Date=2020/02/08 | |Start Date=2020/02/08 | ||
|End Date=2020/02/11 | |End Date=2020/02/11 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=on site | ||
}} | }} | ||
== Topics == | == Topics == | ||
Revision as of 13:38, 6 September 2022
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.
