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)