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{{Event | {{Event | ||
|Acronym=ALT 2021 | |Acronym=ALT 2021 | ||
− | |Title= | + | |Title=International Conference on Algorithmic Learning Theory |
|Ordinal=32 | |Ordinal=32 | ||
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|In Event Series=Event Series:ALT | |In Event Series=Event Series:ALT | ||
|Single Day Event=no | |Single Day Event=no | ||
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|Event Status=as scheduled | |Event Status=as scheduled | ||
|Event Mode=online | |Event Mode=online | ||
+ | |Official Website=http://algorithmiclearningtheory.org/alt2021/# | ||
+ | |Type=Conference | ||
+ | |pageCreator=User:Curator 27 | ||
+ | |pageEditor=User:Curator 27 | ||
+ | |contributionType=1 | ||
}} | }} | ||
− | + | {{Event Deadline}} | |
+ | {{Event Metric}} | ||
+ | {{S Event}} | ||
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The Algorithmic Learning Theory (ALT) 2021 conference will be held in Paris, France on March 16–19, 2021. The conference is dedicated to all theoretical and algorithmic aspects of machine learning. | The Algorithmic Learning Theory (ALT) 2021 conference will be held in Paris, France on March 16–19, 2021. The conference is dedicated to all theoretical and algorithmic aspects of machine learning. | ||
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==Topics== | ==Topics== | ||
* Design and analysis of learning algorithms. | * Design and analysis of learning algorithms. |
Latest revision as of 08:52, 1 November 2022
Deadlines
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Venue
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The Algorithmic Learning Theory (ALT) 2021 conference will be held in Paris, France on March 16–19, 2021. The conference is dedicated to all theoretical and algorithmic aspects of machine learning.
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. * Privacy-preserving data analysis. * Learning with additional societal considerations: e.g., fairness, economics. * Robustness of learning algorithms to adversarial agents. * Artificial neural networks, including deep learning. * High-dimensional and non-parametric statistics. * Adaptive data analysis and selective inference. * Learning with algebraic or combinatorial structure. * Bayesian methods in learning. * Learning in distributed and streaming settings. * Game theory and learning. * Learning from complex data: e.g., networks, time series. * Theoretical analysis of probabilistic graphical models. *