Titlebook: Braverman Readings in Machine Learning. Key Ideas from Inception to Current State; International Confer Lev Rozonoer,Boris Mirkin,Ilya Much

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Conformal Predictive Distributions with Kernelsother new. The first development is bringing predictive distributions into machine learning, whose early development was so deeply influenced by two remarkable groups at the Institute of Automation and Remote Control. As result, they become more robust and their validity ceases to depend on Bayesian

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On the Concept of Compositional Complexitye 3rd chapter of the book of M.A. Aiserman, E.M. Braverman and L.I. Rozonoer, The Method of Potential Functions in Machine Learning Theory, Physical-Mathematical State Publishing, Moscow (1970) – a chapter dedicated to the choice of a potential function. I.B. Muchnik argued the need for the presenta

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On the Choice of a Kernel Function in Symmetric Spacese of Russia. We present here a sligtly abridged version of Chapter III.3 of the book. Technical details of some proofs are omitted and replaced by a short sketch of the main steps of the proof. An interested reader can either fill those details or consult the original Russian edition.

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Causality Modeling and Statistical Generative Mechanismscal inference in probabilistic terms is linked with causality? What modern causality models offer that is substantially different from the traditional dependency models like regression or decision trees, and if yes, do they deliver these promises? How causality models are related to statistical and

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