AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas. The primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. We encourage the submission of all papers which are in keeping with this objective. 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
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