Braverman Readings in Machine Learning. Key Ideas from Inception to Current State

Chapter 2 Braverman’s Spectrum and Matrix Diagonalization Versus iK-Means: A Unified Framework for Clustering

Chapter 3 Compactness Hypothesis, Potential Functions, and Rectifying Linear Space in Machine Learning

Chapter 4 Conformal Predictive Distributions with Kernels

Chapter 5 On the Concept of Compositional Complexity

Chapter 6 On the Choice of a Kernel Function in Symmetric Spaces

Chapter 7 Causality Modeling and Statistical Generative Mechanisms

Chapter 8 One-Class Semi-supervised Learning

Chapter 9 Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients

Chapter 10 On One Approach to Robot Motion Planning

Chapter 11 Geometrical Insights for Implicit Generative Modeling

Chapter 12 Deep Learning in the Natural Sciences: Applications to Physics

Chapter 13 From Reinforcement Learning to Deep Reinforcement Learning: An Overview

Chapter 14 A Man of Unlimited Capabilities (in Memory of E. M. Braverman)

Chapter 15 Braverman and His Theory of Disequilibrium Economics

Chapter 16 Misha Braverman: My Mentor and My Model

Chapter 17 List of Braverman’s Papers Published in the “Avtomatika i telemekhanika” Journal, Moscow, Russia, and Translated to English as “Automation and Remote Control” Journal