ALT 2021

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Revision as of 10:03, 29 July 2026 by Akhilesh (talk | contribs) (Cleaned ALT 2021 with new text {{Event |Acronym=ALT 2021 |Title=32nd International Conference on Algorithmic Learning Theory |Ordinal=32 |Series=ALT |Type=Conference |Field=Machine learning |Start date=2021/03/16 |End date=2021/03/19 |Homepage URL=http://algorithmiclearningtheory.org/alt2021/# |City=Paris |Country=France |Submitted papers=157 |Accepted papers=46 }} '' The Algorithmic Learning Theory (ALT) 2021 conference will be held in Paris, France on March 16–19, 2021. The conference is de...)
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ALT 2021
32nd International Conference on Algorithmic Learning Theory
Ordinal 32
Type Conference
Field: Machine learning
Event in series ALT
Dates 2021/03/16 (iCal) - 2021/03/19
Location
Location: Paris,France
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Bibliographic and retrospective information
Submitted Submitted 157
Accepted Accepted 46
Acceptance rate Acceptance rate 29.3%
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.
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