The 33rd Annual Conference on Learning Theory (COLT 2020)

Country: Austria

City: Graz

Abstr. due: 31.01.2020

Dates: 09.07.20 — 12.07.20

Area Of Sciences: Pedagogy;

Organizing comittee e-mail: TBA here: http://learningtheory.org/colt2020/.

Organizers: Association for Computational Learning

 

The 33rd Annual Conference on Learning Theory (COLT 2020) will take place in Graz, Austria during July 9-12, 2020. We invite submissions of papers addressing theoretical aspects of machine learning and related topics. We strongly support a broad definition of learning theory, including, but not limited to:

  • Design and analysis of learning algorithms
  • Statistical and computational complexity of learning
  • Optimization methods for learning, and/or online and/or stochastic optimization
  • Supervised learning
  • Unsupervised and semi-supervised learning
  • Active and interactive learning
  • Reinforcement learning
  • Online learning and decision-making
  • Interactions of learning theory with other mathematical fields
  • Theory of artificial neural networks, including (theory of) deep learning
  • High-dimensional and non-parametric statistics
  • Learning with algebraic or combinatorial structure
  • 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 other settings (e.g., computational social science, economics)

Submissions by authors who are new to COLT are encouraged. While the primary focus of the conference is theoretical, authors may support their analysis by including relevant experimental results.

 

Conference Web-Site: http://learningtheory.org/colt2020/cfp.html

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