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An Introduction to Variational Autoencoders
In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep …
Adaptation, Learning, and Optimization over Networks
Adaptation, Learning, and Optimization over Networks deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that …
User-friendly Introduction to PAC-Bayes Bounds
Probably almost correct (PAC) bounds have been an intensive field of research over the last two decades. Hundreds of papers have been published and much progress has been made …
Introduction to Multi-Armed Bandits
Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first monograph to provide a …
Spectral Methods for Data Science
In contemporary science and engineering applications, the volume of available data is growing at an enormous rate. Spectral methods have emerged as a simple yet surprisingly …
Dynamical Variational Autoencoders
Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an …
From Bandits to Monte-Carlo Tree Search
From Bandits to Monte-Carlo Tree Search covers several aspects of the ""optimism in the face of uncertainty"" principle for large scale optimization problems under finite numerical …
Risk-Sensitive Reinforcement Learning via Policy Gradient Search
Reinforcement learning (RL) is one of the foundational pillars of artificial intelligence and machine learning. An important consideration in any optimization or control problem is …
An Introduction to Matrix Concentration Inequalities
Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. It is therefore desirable to have tools for studying random matrices that are …
A Survey of Statistical Network Models
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major …
Model-based Reinforcement Learning
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this …
A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists
Spin glass models were introduced by physicists in the 1970s to model the statistical properties of certain magnetic materials. Over the last half century, these models have …