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