Topics of interest include (but are not limited to):

    general machine learning (active learning, clustering, online learning, ranking, supervised, semi- and self-supervised learning, time series analysis, etc.)
    deep learning (architectures, generative models, theory, etc.)
    evaluation (methodology, meta studies, replicability and validity, human-in-the-loop, etc.)
    theory of machine learning (statistical learning theory, bandits, game theory, decision theory, etc.)
    machine learning systems (improved implementation and scalability, hardware, libraries, distributed methods, etc.)
    optimization (convex and non-convex optimization, matrix/tensor methods, stochastic, online, non-smooth, composite, etc.)
    probabilistic methods (Bayesian methods, graphical models, Monte Carlo methods, etc.)
    reinforcement learning (decision and control, planning, hierarchical RL, robotics, etc.)
    trustworthy machine learning (reliability, causality, fairness, interpretability, privacy, robustness, safety, etc.)
    application-driven machine learning (innovative techniques, problems, and datasets that are of interest to the machine learning community and driven by the needs of end-users in applications such as healthcare, physical sciences, biosciences, social sciences, sustainability, and climate etc.)