Foundations and Trends in Machine Learning

ISSN: 1935-8237

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Foundations and Trends in Machine Learning Q1 Unclaimed

Now Publishers Inc United States
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Electronic publishing has given researchers instant access to more articles than ever before. But which articles are the essential ones that should be read to understand and keep abreast with developments of any topic? To address this problem Foundations and Trends® in Machine Learning publishes high-quality survey and tutorial monographs of the field. Foundations and Trends® in Machine Learning publishes survey and tutorial articles on the theory, algorithms and applications of machine learning

Type: Journal

Type of Copyright:

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Open Access Policy: Non Open Access

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Publication frecuency: -

Price

- €

Gold OA

-

Green OA

0 €

Non OA

Metrics

Foundations and Trends in Machine Learning

5,12

SJR Impact factor

30

H Index

2

Total Docs (Last Year)

10

Total Docs (3 years)

752

Total Refs

198

Total Cites (3 years)

10

Citable Docs (3 years)

18,57

Cites/Doc (2 years)

376

Ref/Doc


Best articles

A Survey of Statistical Network Models

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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning

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A Tutorial on Thompson Sampling

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Adaptation, Learning, and Optimization over Networks

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An Introduction to Conditional Random Fields

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An Introduction to Deep

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An Introduction to Matrix Concentration Inequalities

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An Introduction to Variational Autoencoders

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An Introduction to Wishart Matrix Moments

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Backward Simulation Methods for Monte Carlo Statistical Inference

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Computational Optimal Transport

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Convex Optimization: Algorithms and Complexity

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Determinantal Point Processes for Machine Learning

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Dimension Reduction: A Guided Tour

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

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Elements of Sequential Monte Carlo

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Explaining the Success of Nearest

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Explicit-Duration Markov Switching Models

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

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Generalized Low Rank Models

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Graphical Models, Exponential Families, and Variational Inference

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Introduction to Multi-Armed Bandits

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Kernel Mean Embedding of Distributions: A Review and Beyond

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Kernels for Vector-Valued Functions: A Review

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