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Covers local averaging predictors, including K-nearest neighbors and Nadaraya-Watson estimators, as well as local linear regression and its applications.
Explores equivariant structural representations in atomistic machine learning, emphasizing the importance of representing target properties in the spherical basis.
Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.
