Foundations and Trends® in Machine Learning
Editors-in-chief
Michael Jordan
University of California, Berkeley
Ryan Tibshirani
University of California, Berkeley
Print ISSN: 1935-8237
Online ISSN: 1935-8245
Publisher
Mike Casey
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
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