COLT 2018 - 30th International Conference on Learning Theory

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 models and algorithms for learning
Unsupervised, semi-supervised, and active learning
Online learning
Artificial neural networks, including deep learning
Learning with large-scale datasets
Decision making under uncertainty
Bayesian methods in learning
High dimensional and non-parametric statistical inference
Planning and control, including reinforcement learning
Learning with additional constraints: e.g. privacy, memory or communication budget
Learning in other settings: e.g. social, economic, and game-theoretic
Analysis and applications of learning theory in related fields: natural language processing, neuroscience, bioinformatics, privacy and security, machine vision, information retrieval
Submissions by authors who are new to COLT are encouraged. While the primary focus of the conference is theoretical, the authors are encouraged to support their analysis by including relevant experimental results.