Difference between revisions of "ALT 2021"
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| − | |Title =32nd International Conference on Algorithmic Learning Theory}} | + | |Title =32nd International Conference on Algorithmic Learning Theory |
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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. | ||
Latest revision as of 03:47, 6 December 2021
Event Rating
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List of all ratings can be found at ALT 2021/rating
| ALT 2021 | |
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32nd International Conference on Algorithmic Learning Theory
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| Ordinal | 32 |
| Event in series | ALT |
| Dates | 2021-03-16 (iCal) - 2021-03-19 |
| Homepage: | http://algorithmiclearningtheory.org/alt2021/# |
| Location | |
| Location: | FR/IDF/Paris, FR/IDF, FR |
| Table of Contents | |
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. *