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Foundations and Trends(r) in Machine Learning

Learning Deep Architectures for AI

Kernels for Vector-Valued Functions: A Review

Optimization with Sparsity-Inducing Penalties

On the Concentration Properties of Interacting Particle Processes

Online Learning and Online Convex Optimization

Learning Representation and Control in Markov Decision Processes

A Survey of Statistical Network Models

Clustering Stability: An Overview

Dimension Reduction: A Guided Tour

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

Randomized Algorithms for Matrices and Data

An Introduction to Conditional Random Fields

Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems

Determinantal Point Processes for Machine Learning

Learning with Submodular Functions: A Convex Optimization Perspective

A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning: A Convex Optimization Perspective

Backward Simulation Methods for Monte Carlo Statistical Inference

Metric Learning

From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning

Theory of Disagreement-Based Active Learning

Adaptation, Learning, and Optimization Over Networks

Convex Optimization

Bayesian Reinforcement Learning

Patterns of Scalable Bayesian Inference

Papetărie, jocuri, reviste

Foundations and Trends(r) in Machine Learning