now publishers - Foundations and Trends® in Machine Learning

Foundations and Trends® in Machine Learning

Editors-in-chief

Michael Jordan

University of California, Berkeley

Personal homepage

Ryan Tibshirani

University of California, Berkeley

Personal homepage

Print ISSN: 1935-8237

Online ISSN: 1935-8245

Publisher

Mike Casey

mike.casey@nowpublishers.com

Indexed in: ACM Guide, Cabell's International, Computing Reviews, DBLP, EI Compendex, Electronic Journals Library, Emerging Sources Citation Index (ESCI), Google Scholar, INSPEC, PubGet, SCOPUS, Ulrich's, Zentralblatt Math

About this journal

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2022 Journal Impact Factor (Clarivate) 32.8

2022 Journal Citation Index (Clarivate) 8.76

Volume 16

Volume 16, Issue 6

Reinforcement Learning, Bit by Bit

Volume 16, Issue 5

Tutorial on Amortized Optimization

Brandon Amos

Volume 16, Issue 4

Conformal Prediction: A Gentle Introduction

Anastasios N. Angelopoulos | Stephen Bates

Volume 16, Issue 3

Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats

Nicolas Guigui | Nina Miolane | Xavier Pennec

Volume 16, Issue 2

Graph Neural Networks for Natural Language Processing: A Survey

Volume 16, Issue 1

Model-based Reinforcement Learning: A Survey

Thomas M. Moerland | Joost Broekens | Aske Plaat | Catholijn M. Jonker

Volume 15

Volume 15, Issue 6

Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems

Ryan J. Tibshirani

Volume 15, Issue 5

Risk-Sensitive Reinforcement Learning via Policy Gradient Search

Prashanth L. A. | Michael C. Fu

Volume 15, Issue 4

A Unifying Tutorial on Approximate Message Passing

Oliver Y. Feng | Ramji Venkataramanan | Cynthia Rush | Richard J. Samworth

Volume 15, Issue 3

Learning in Repeated Auctions

Thomas Nedelec | Clément Calauzènes | Noureddine El Karoui | Vianney Perchet

Volume 15, Issue 1-2

Dynamical Variational Autoencoders: A Comprehensive Review

Volume 14

Volume 14, Issue 6

Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT

Sean B. Holden

Volume 14, Issue 5

Spectral Methods for Data Science: A Statistical Perspective

Yuxin Chen | Yuejie Chi | Jianqing Fan | Cong Ma

Volume 14, Issue 4

Tensor Regression

Jiani Liu | Ce Zhu | Zhen Long | Yipeng Liu

Volume 14, Issue 3

Minimum-Distortion Embedding

Akshay Agrawal | Alnur Ali | Stephen Boyd

Volume 14, Issue 1–2

Advances and Open Problems in Federated Learning

Volume 13

Volume 13, Issue 5-6

Graph Kernels: State-of-the-Art and Future Challenges

Karsten Borgwardt | Elisabetta Ghisu | Felipe Llinares-López | Leslie O’Bray | Bastian Rieck

Volume 13, Issue 4

Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications

Volume 13, Issue 2-3

Data Analytics on Graphs Part II: Signals on Graphs

Volume 13, Issue 1

Data Analytics on Graphs Part I: Graphs and Spectra on Graphs

Volume 12

Volume 12, Issue 5-6

Spectral Learning on Matrices and Tensors

Majid Janzamin | Rong Ge | Jean Kossaifi | Anima Anandkumar

Volume 12, Issue 4

An Introduction to Variational Autoencoders

Diederik P. Kingma | Max Welling

Volume 12, Issue 3

Elements of Sequential Monte Carlo

Christian A. Naesseth | Fredrik Lindsten | Thomas B. Schön

Volume 12, Issue 1-2

Introduction to Multi-Armed Bandits

Aleksandrs Slivkins

Volume 11

Volume 11, Issue 5-6

Computational Optimal Transport: With Applications to Data Science

Gabriel Peyré | Marco Cuturi

Volume 11, Issue 3-4

An Introduction to Deep Reinforcement Learning

Vincent François-Lavet | Peter Henderson | Riashat Islam | Marc G. Bellemare | Joelle Pineau

Volume 11, Issue 2

An Introduction to Wishart Matrix Moments

Adrian N. Bishop | Pierre Del Moral | Angèle Niclas

Volume 11, Issue 1

A Tutorial on Thompson Sampling

Daniel J. Russo | Benjamin Van Roy | Abbas Kazerouni | Ian Osband | Zheng Wen

Volume 10

Volume 10, Issue 5-6

Explaining the Success of Nearest Neighbor Methods in Prediction

George H. Chen | Devavrat Shah

Volume 10, Issue 3-4

Non-convex Optimization for Machine Learning

Prateek Jain | Purushottam Kar

Volume 10, Issue 1-2

Kernel Mean Embedding of Distributions: A Review and Beyond

Krikamol Muandet | Kenji Fukumizu | Bharath Sriperumbudur | Bernhard Schölkopf

Volume 9

Volume 9, Issue 6

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives

Volume 9, Issue 4-5

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions

Volume 9, Issue 2-3

Patterns of Scalable Bayesian Inference

Elaine Angelino | Matthew James Johnson | Ryan P. Adams

Volume 9, Issue 1

Generalized Low Rank Models

Madeleine Udell | Corinne Horn | Reza Zadeh | Stephen Boyd

Volume 8

Volume 8, Issue 5-6

Bayesian Reinforcement Learning: A Survey

Mohammad Ghavamzadeh | Shie Mannor | Joelle Pineau | Aviv Tamar

Volume 8, Issue 3-4

Convex Optimization: Algorithms and Complexity

Sébastien Bubeck

Volume 8, Issue 1-2

An Introduction to Matrix Concentration Inequalities

Joel A. Tropp

Volume 7

Volume 7, Issue 6

Explicit-Duration Markov Switching Models

Silvia Chiappa

Volume 7, Issue 4-5

Adaptation, Learning, and Optimization over Networks

Ali H. Sayed

Volume 7, Issue 2-3

Theory of Disagreement-Based Active Learning

Steve Hanneke

Volume 7, Issue 1

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

Rémi Munos

Volume 6

Volume 6, Issue 4

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

Volume 6, Issue 2-3

Learning with Submodular Functions: A Convex Optimization Perspective

Francis Bach

Volume 6, Issue 1

Backward Simulation Methods for Monte Carlo Statistical Inference

Fredrik Lindsten | Thomas B. Schön

Volume 5

Volume 5, Issue 4

Metric Learning: A Survey

Brian Kulis

Volume 5, Issue 2–3

Determinantal Point Processes for Machine Learning

Alex Kulesza | Ben Taskar

Volume 5, Issue 1

Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems

Sébastien Bubeck | Nicolò Cesa-Bianchi

Volume 4

Volume 4, Issue 4

An Introduction to Conditional Random Fields

Charles Sutton | Andrew McCallum

Volume 4, Issue 3

Kernels for Vector-Valued Functions: A Review

Mauricio A. Álvarez | Lorenzo Rosasco | Neil D. Lawrence

Volume 4, Issue 2

Online Learning and Online Convex Optimization

Shai Shalev-Shwartz

Volume 4, Issue 1

Optimization with Sparsity-Inducing Penalties

Francis Bach | Rodolphe Jenatton | Julien Mairal | Guillaume Obozinski

Volume 3

Volume 3, Issue 3–4

On the Concentration Properties of Interacting Particle Processes

Pierre Del Moral | Peng Hu | Liming Wu

Volume 3, Issue 2

Randomized Algorithms for Matrices and Data

Michael W. Mahoney

Volume 3, Issue 1

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

Stephen Boyd | Neal Parikh | Eric Chu | Borja Peleato | Jonathan Eckstein

Volume 2

Volume 2, Issue 4

Dimension Reduction: A Guided Tour

Christopher J. C. Burges

Volume 2, Issue 3

Clustering Stability: An Overview

Ulrike von Luxburg

Volume 2, Issue 2

A Survey of Statistical Network Models

Anna Goldenberg | Alice X. Zheng | Stephen E. Fienberg | Edoardo M. Airoldi

Volume 2, Issue 1

Learning Deep Architectures for AI

Yoshua Bengio

Volume 1

Volume 1, Issue 4

Learning Representation and Control in Markov Decision Processes: New Frontiers

Sridhar Mahadevan

Volume 1, Issue 3

Property Testing: A Learning Theory Perspective

Dana Ron

Volume 1, Issue 1–2

Graphical Models, Exponential Families, and Variational Inference

Martin J. Wainwright | Michael I. Jordan