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
    Causality
    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