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This workshop brings together leading researchers to examine advances in statistical learning beyond classical assumptions such as independence, identical distribution, and low-dimensional Euclidean structure. Topics include learning with dependent and heterogeneous data, stochastic processes, functional-geometrical-topological data, etc. The emphasis is on asymptotic and nonasymptotic theory, optimization methods, and applications to complex real-world problems.