Foundations and Trends® in Machine Learning
Editor-in-chief
Ryan Tibshirani
University of California, Berkeley
Founding Editor
Michael Jordan, University of California, Berkeley
Print ISSN: 1935-8237
Online ISSN: 1935-8245
Publisher
Mark de Jongh
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
2023 Journal Impact Factor (Clarivate)
65.3
2023 Journal Citation Indicator (Clarivate)
6.3
Volume 17, Issue 6
George H. Chen
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes
Volume 17, Issue 5
Xuanyi Dong | David Jacob Kedziora | Katarzyna Musial | Bogdan Gabrys
Automated Deep Learning: Neural Architecture Search Is Not the End
Volume 17, Issue 4
David Jacob Kedziora | Katarzyna Musial | Bogdan Gabrys
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
Volume 16, Issue 6
Xiuyuan Lu | Benjamin Van Roy | Vikranth Dwaracherla | Morteza Ibrahimi | Ian Osband | Zheng Wen
Reinforcement Learning, Bit by Bit
Volume 16, Issue 4
Anastasios N. Angelopoulos | Stephen Bates
Conformal Prediction: A Gentle Introduction
Volume 16, Issue 3
Nicolas Guigui | Nina Miolane | Xavier Pennec
Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats
Volume 16, Issue 2
Lingfei Wu | Yu Chen | Kai Shen | Xiaojie Guo | Hanning Gao | Shucheng Li | Jian Pei | Bo Long
Graph Neural Networks for Natural Language Processing: A Survey
Volume 16, Issue 1
Thomas M. Moerland | Joost Broekens | Aske Plaat | Catholijn M. Jonker
Model-based Reinforcement Learning: A Survey
Volume 15, Issue 6
Ryan J. Tibshirani
Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems
Volume 15, Issue 5
Prashanth L. A. | Michael C. Fu
Risk-Sensitive Reinforcement Learning via Policy Gradient Search
Volume 15, Issue 4
Oliver Y. Feng | Ramji Venkataramanan | Cynthia Rush | Richard J. Samworth
A Unifying Tutorial on Approximate Message Passing
Volume 15, Issue 3
Thomas Nedelec | Clément Calauzènes | Noureddine El Karoui | Vianney Perchet
Learning in Repeated Auctions
Volume 15, Issue 1-2
Laurent Girin | Simon Leglaive | Xiaoyu Bie | Julien Diard | Thomas Hueber | Xavier Alameda-Pineda
Dynamical Variational Autoencoders: A Comprehensive Review
Volume 14, Issue 6
Sean B. Holden
Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT
Volume 14, Issue 5
Yuxin Chen | Yuejie Chi | Jianqing Fan | Cong Ma
Spectral Methods for Data Science: A Statistical Perspective
Volume 14, Issue 1–2
Peter Kairouz | H. Brendan McMahan | Brendan Avent | Aurélien Bellet | Mehdi Bennis | Arjun Nitin Bhagoji | Kallista Bonawitz | Zachary Charles | Graham Cormode | Rachel Cummings | Rafael G. L. D’Oliveira | Hubert Eichner | Salim El Rouayheb | David Evans | Josh Gardner | Zachary Garrett | Adrià Gascón | Badih Ghazi | Phillip B. Gibbons | Marco Gruteser | Zaid Harchaoui | Chaoyang He | Lie He | Zhouyuan Huo | Ben Hutchinson | Justin Hsu | Martin Jaggi | Tara Javidi | Gauri Joshi | Mikhail Khodak | Jakub Konecný | Aleksandra Korolova | Farinaz Koushanfar | Sanmi Koyejo | Tancrède Lepoint | Yang Liu | Prateek Mittal | Mehryar Mohri | Richard Nock | Ayfer Özgür | Rasmus Pagh | Hang Qi | Daniel Ramage | Ramesh Raskar | Mariana Raykova | Dawn Song | Weikang Song | Sebastian U. Stich | Ziteng Sun | Ananda Theertha Suresh | Florian Tramèr | Praneeth Vepakomma | Jianyu Wang | Li Xiong | Zheng Xu | Qiang Yang | Felix X. Yu | Han Yu | Sen Zhao
Advances and Open Problems in Federated Learning
Volume 13, Issue 5-6
Karsten Borgwardt | Elisabetta Ghisu | Felipe Llinares-López | Leslie O’Bray | Bastian Rieck
Graph Kernels: State-of-the-Art and Future Challenges
Volume 13, Issue 4
Ljubiša Stanković | Danilo Mandic | Miloš Daković | Miloš Brajović | Bruno Scalzo | Shengxi Li | Anthony G. Constantinides
Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications
Volume 13, Issue 2-3
Ljubiša Stanković | Danilo Mandic | Miloš Daković | Miloš Brajović | Bruno Scalzo | Shengxi Li | Anthony G. Constantinides
Data Analytics on Graphs Part II: Signals on Graphs
Volume 12, Issue 5-6
Majid Janzamin | Rong Ge | Jean Kossaifi | Anima Anandkumar
Spectral Learning on Matrices and Tensors
Volume 12, Issue 3
Christian A. Naesseth | Fredrik Lindsten | Thomas B. Schön
Elements of Sequential Monte Carlo
Volume 11, Issue 5-6
Gabriel Peyré | Marco Cuturi
Computational Optimal Transport: With Applications to Data Science
Volume 11, Issue 3-4
Vincent François-Lavet | Peter Henderson | Riashat Islam | Marc G. Bellemare | Joelle Pineau
An Introduction to Deep Reinforcement Learning
Volume 11, Issue 2
Adrian N. Bishop | Pierre Del Moral | Angèle Niclas
An Introduction to Wishart Matrix Moments
Volume 11, Issue 1
Daniel J. Russo | Benjamin Van Roy | Abbas Kazerouni | Ian Osband | Zheng Wen
A Tutorial on Thompson Sampling
Volume 10, Issue 5-6
George H. Chen | Devavrat Shah
Explaining the Success of Nearest Neighbor Methods in Prediction
Volume 9, Issue 6
Andrzej Cichocki | Anh-Huy Phan | Qibin Zhao | Namgil Lee | Ivan Oseledets | Masashi Sugiyama | Danilo P. Mandic
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives
Volume 9, Issue 4-5
Andrzej Cichocki | Namgil Lee | Ivan Oseledets | Anh-Huy Phan | Qibin Zhao | Danilo P. Mandic
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
Volume 9, Issue 2-3
Elaine Angelino | Matthew James Johnson | Ryan P. Adams
Patterns of Scalable Bayesian Inference
Volume 9, Issue 1
Madeleine Udell | Corinne Horn | Reza Zadeh | Stephen Boyd
Generalized Low Rank Models
Volume 8, Issue 5-6
Mohammad Ghavamzadeh | Shie Mannor | Joelle Pineau | Aviv Tamar
Bayesian Reinforcement Learning: A Survey
Volume 6, Issue 4
Alborz Geramifard | Thomas J. Walsh | Stefanie Tellex | Girish Chowdhary | Nicholas Roy | Jonathan P. How
A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
Volume 6, Issue 2-3
Francis Bach
Learning with Submodular Functions: A Convex Optimization Perspective
Volume 4, Issue 3
Mauricio A. Álvarez | Lorenzo Rosasco | Neil D. Lawrence
Kernels for Vector-Valued Functions: A Review
Volume 4, Issue 1
Francis Bach | Rodolphe Jenatton | Julien Mairal | Guillaume Obozinski
Optimization with Sparsity-Inducing Penalties
Volume 3, Issue 3–4
Pierre Del Moral | Peng Hu | Liming Wu
On the Concentration Properties of Interacting Particle Processes
Volume 2, Issue 2
Anna Goldenberg | Alice X. Zheng | Stephen E. Fienberg | Edoardo M. Airoldi
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning