Solicited topics include, but are not limited to:

• Models and estimation: graphical models, causality, Gaussian processes, approximate inference, kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing, ...

• Classification, regression, density estimation, unsupervised and semi-supervised learning, clustering, topic models, ...

• Structured prediction, relational learning, logic and probability

• Reinforcement learning, planning, control

• Game theory, no-regret learning, multi-agent systems

• Algorithms and architectures for high-performance computation in AI and statistics

• Software for and applications of AI and statistics

• Deep learning including optimization, generalization and architectures

• Trustworthy learning, including learning with privacy and fairness, interpretability, and robustness