We consider a broad range of subject areas including feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, uncertainty quantification and issues regarding large-scale learning and non-convex optimization, as well as applications in vision, audio, speech, language, music, robotics, games, healthcare, biology, sustainability, economics, ethical considerations in ML, and others.

A non-exhaustive list of relevant topics:

    unsupervised, self-supervised, semi-supervised, and supervised representation learning
    transfer learning, meta learning, and lifelong learning
    reinforcement learning
    representation learning for computer vision, audio, language, and other modalities
    metric learning, kernel learning
    probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
    generative models
    causal reasoning
    optimization
    learning theory
    learning on graphs and other geometries & topologies
    societal considerations including fairness, safety, privacy
    visualization or interpretation of learned representations
    datasets and benchmarks
    infrastructure, software libraries, hardware, etc.
    neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
    applications to robotics, autonomy, planning
    applications to neuroscience & cognitive science
    applications to physical sciences (physics, chemistry, biology, etc.)
    general machine learning (i.e., none of the above)