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COLT 2023 : Computational Learning Theory


Conference Series : Computational Learning Theory
When Jul 19, 2023 - Jul 22, 2023
Where Bangalore
Submission Deadline Feb 10, 2023
Notification Due May 15, 2023
Categories    machine learning   theory

Call For Papers

The 36th Annual Conference on Learning Theory (COLT 2023) will take place July 19th-22nd, 2023. Assuming the circumstances allow for an in-person conference it will be held in Bangalore, India. We invite submissions of papers addressing theoretical aspects of machine learning, broadly defined as a subject at the intersection of computer science, statistics and applied mathematics. We strongly support an inclusive view of learning theory, including fundamental theoretical aspects of learnability in various contexts, and theory that sheds light on empirical phenomena.

The topics include but are not limited to:

Design and analysis of learning algorithms
Statistical and computational complexity of learning
Optimization methods for learning, including online and stochastic optimization
Theory of artificial neural networks, including deep learning
Theoretical explanation of empirical phenomena in learning
Supervised learning
Unsupervised, semi-supervised learning, domain adaptation
Learning geometric and topological structures in data, manifold learning
Active and interactive learning
Reinforcement learning
Online learning and decision-making
Interactions of learning theory with other mathematical fields
High-dimensional and non-parametric statistics
Kernel methods
Theoretical analysis of probabilistic graphical models
Bayesian methods in learning
Game theory and learning
Learning with system constraints (e.g., privacy, fairness, memory, communication)
Learning from complex data (e.g., networks, time series)
Learning in neuroscience, social science, economics and other subjects

Submissions by authors who are new to COLT are encouraged.

While the primary focus of the conference is theoretical, authors are welcome to support their analysis with relevant experimental results.

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