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All papers submitted to the conference will undergo rigorous and unbiased peer review process conducted by experts of the research area that the work of authors focuses. We invite submissions on theoretical and applicable research falls within the conference scope. The topics of interest include, but are not limited to:
Approximate inference Bayesian learning Business process intelligence Causal models Classification Clustering Density estimation Federated learning Gaussian processes Generalization and regularization Graphical models High-performance computation Intelligent optimization Kernel and spectral methods Large-scale optimization algorithm | Logic and probability Manifolds learning Multi-agent systems Non-Bayesian models and estimation Nonparametric models No-regret learning Optimization for deep learning Privacy-preserving learning Reinforcement learning Software and applications of artificial intelligence Sparsity and compressed sensing Statistical and computational learning theory Statistical optimization Structural learning and prediction Unsupervised and semi-supervised learning |