Found. Trends Mach. Learn.

Keyphrases

Publications

volume 17, number 1, 2024

Andrea Montanari, Subhabrata Sen
A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists. Found. Trends Mach. Learn. 17 (1) (2024)

volume 17, number 2, 2024

Pierre Alquier
User-friendly Introduction to PAC-Bayes Bounds. Found. Trends Mach. Learn. 17 (2) (2024)

volume 17, number 3, 2024

Drago Plecko, Elias Bareinboim
Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning. Found. Trends Mach. Learn. 17 (3) (2024)

volume 16, number 1, 2023

Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker
Model-based Reinforcement Learning: A Survey. Found. Trends Mach. Learn. 16 (1) (2023)

volume 16, number 2, 2023

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. Found. Trends Mach. Learn. 16 (2) (2023)

volume 16, number 3, 2023

Nicolas Guigui, Nina Miolane, Xavier Pennec
Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats. Found. Trends Mach. Learn. 16 (3) (2023)

volume 16, number 4, 2023

Anastasios N. Angelopoulos, Stephen Bates
Conformal Prediction: A Gentle Introduction. Found. Trends Mach. Learn. 16 (4) (2023)

volume 16, number 5, 2023

Brandon Amos
Tutorial on Amortized Optimization. Found. Trends Mach. Learn. 16 (5) (2023)

volume 16, number 6, 2023

Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen
Reinforcement Learning, Bit by Bit. Found. Trends Mach. Learn. 16 (6) (2023)

volume 15, number 3, 2022

Thomas Nedelec, Clément Calauzènes, Noureddine El Karoui, Vianney Perchet
Learning in Repeated Auctions. Found. Trends Mach. Learn. 15 (3) (2022)

volume 15, number 4, 2022

Oliver Y. Feng, Ramji Venkataramanan, Cynthia Rush, Richard J. Samworth
A Unifying Tutorial on Approximate Message Passing. Found. Trends Mach. Learn. 15 (4) (2022)

volume 15, number 5, 2022

Prashanth L. A., Michael C. Fu
Risk-Sensitive Reinforcement Learning via Policy Gradient Search. Found. Trends Mach. Learn. 15 (5) (2022)

volume 15, number 6, 2022

Ryan J. Tibshirani
Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. Found. Trends Mach. Learn. 15 (6) (2022)

volume 15, number 1-2, 2021

Laurent Girin, Simon Leglaive, Xiaoyu Bie, Julien Diard, Thomas Hueber, Xavier Alameda-Pineda
Dynamical Variational Autoencoders: A Comprehensive Review. Found. Trends Mach. Learn. 15 (1-2) (2021)

volume 14, number 1-2, 2021

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. 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, Zaïd 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. Found. Trends Mach. Learn. 14 (1-2) (2021)

volume 14, number 3, 2021

Akshay Agrawal, Alnur Ali, Stephen P. Boyd
Minimum-Distortion Embedding. Found. Trends Mach. Learn. 14 (3) (2021)

volume 14, number 4, 2021

Jiani Liu, Ce Zhu, Zhen Long, Yipeng Liu
Tensor Regression. Found. Trends Mach. Learn. 14 (4) (2021)

volume 14, number 5, 2021

Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma
Spectral Methods for Data Science: A Statistical Perspective. Found. Trends Mach. Learn. 14 (5) (2021)

volume 14, number 6, 2021

Sean B. Holden
Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT. Found. Trends Mach. Learn. 14 (6) (2021)

volume 13, number 1, 2020

Ljubisa Stankovic, Danilo P. Mandic, Milos Dakovic, Milos Brajovic, Bruno Scalzo, Shengxi Li, Anthony G. Constantinides
Data Analytics on Graphs Part I: Graphs and Spectra on Graphs. Found. Trends Mach. Learn. 13 (1) (2020)

volume 13, number 2-3, 2020

Ljubisa Stankovic, Danilo P. Mandic, Milos Dakovic, Milos Brajovic, Bruno Scalzo, Shengxi Li, Anthony G. Constantinides
Data Analytics on Graphs Part II: Signals on Graphs. Found. Trends Mach. Learn. 13 (2-3) (2020)

volume 13, number 4, 2020

Ljubisa Stankovic, Danilo P. Mandic, Milos Dakovic, Milos Brajovic, Bruno Scalzo, Shengxi Li, Anthony G. Constantinides
Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications. Found. Trends Mach. Learn. 13 (4) (2020)

volume 13, number 5-6, 2020

Karsten M. Borgwardt, M. Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck
Graph Kernels: State-of-the-Art and Future Challenges. Found. Trends Mach. Learn. 13 (5-6) (2020)

volume 12, number 1-2, 2019

Aleksandrs Slivkins
Introduction to Multi-Armed Bandits. Found. Trends Mach. Learn. 12 (1-2) (2019)

volume 12, number 3, 2019

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
Elements of Sequential Monte Carlo. Found. Trends Mach. Learn. 12 (3) (2019)

volume 12, number 4, 2019

Diederik P. Kingma, Max Welling
An Introduction to Variational Autoencoders. Found. Trends Mach. Learn. 12 (4) (2019)

volume 12, number 5-6, 2019

Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar
Spectral Learning on Matrices and Tensors. Found. Trends Mach. Learn. 12 (5-6) (2019)

volume 11, number 5-6, 2019

Gabriel Peyré, Marco Cuturi
Computational Optimal Transport. Found. Trends Mach. Learn. 11 (5-6) (2019)

volume 11, number 1, 2018

Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen
A Tutorial on Thompson Sampling. Found. Trends Mach. Learn. 11 (1) (2018)

volume 11, number 2, 2018

Adrian N. Bishop, Pierre Del Moral, Angèle Niclas
An Introduction to Wishart Matrix Moments. Found. Trends Mach. Learn. 11 (2) (2018)

volume 11, number 3-4, 2018

Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau
An Introduction to Deep Reinforcement Learning. Found. Trends Mach. Learn. 11 (3-4) (2018)

volume 10, number 5-6, 2018

George H. Chen, Devavrat Shah
Explaining the Success of Nearest Neighbor Methods in Prediction. Found. Trends Mach. Learn. 10 (5-6) (2018)

volume 10, number 1-2, 2017

Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf
Kernel Mean Embedding of Distributions: A Review and Beyond. Found. Trends Mach. Learn. 10 (1-2) (2017)

volume 10, number 3-4, 2017

Prateek Jain, Purushottam Kar
Non-convex Optimization for Machine Learning. Found. Trends Mach. Learn. 10 (3-4) (2017)

volume 9, number 6, 2017

Andrzej Cichocki, Anh Huy Phan, Qibin Zhao, Namgil Lee, Ivan V. Oseledets, Masashi Sugiyama, Danilo P. Mandic
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives. Found. Trends Mach. Learn. 9 (6) (2017)

volume 9, number 1, 2016

Madeleine Udell, Corinne Horn, Reza Zadeh, Stephen P. Boyd
Generalized Low Rank Models. Found. Trends Mach. Learn. 9 (1) (2016)

volume 9, number 2-3, 2016

Elaine Angelino, Matthew James Johnson, Ryan P. Adams
Patterns of Scalable Bayesian Inference. Found. Trends Mach. Learn. 9 (2-3) (2016)

volume 9, number 4-5, 2016

Andrzej Cichocki, Namgil Lee, Ivan V. Oseledets, Anh Huy Phan, Qibin Zhao, Danilo P. Mandic
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. Found. Trends Mach. Learn. 9 (4-5) (2016)

volume 8, number 1-2, 2015

Joel A. Tropp
An Introduction to Matrix Concentration Inequalities. Found. Trends Mach. Learn. 8 (1-2) (2015)

volume 8, number 3-4, 2015

Sébastien Bubeck
Convex Optimization: Algorithms and Complexity. Found. Trends Mach. Learn. 8 (3-4) (2015)

volume 8, number 5-6, 2015

Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar
Bayesian Reinforcement Learning: A Survey. Found. Trends Mach. Learn. 8 (5-6) (2015)

volume 7, number 1, 2014

Rémi Munos
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning. Found. Trends Mach. Learn. 7 (1) (2014)

volume 7, number 2-3, 2014

Steve Hanneke
Theory of Disagreement-Based Active Learning. Found. Trends Mach. Learn. 7 (2-3) (2014)

volume 7, number 4-5, 2014

Ali H. Sayed
Adaptation, Learning, and Optimization over Networks. Found. Trends Mach. Learn. 7 (4-5) (2014)

volume 7, number 6, 2014

Silvia Chiappa
Explicit-Duration Markov Switching Models. Found. Trends Mach. Learn. 7 (6) (2014)

volume 6, number 1, 2013

Fredrik Lindsten, Thomas B. Schön
Backward Simulation Methods for Monte Carlo Statistical Inference. Found. Trends Mach. Learn. 6 (1) (2013)

volume 6, number 2-3, 2013

Francis R. Bach
Learning with Submodular Functions: A Convex Optimization Perspective. Found. Trends Mach. Learn. 6 (2-3) (2013)

volume 6, number 4, 2013

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. Found. Trends Mach. Learn. 6 (4) (2013)

volume 5, number 4, 2013

Brian Kulis
Metric Learning: A Survey. Found. Trends Mach. Learn. 5 (4) (2013)

volume 5, number 2-3, 2012

Alex Kulesza, Ben Taskar
Determinantal Point Processes for Machine Learning. Found. Trends Mach. Learn. 5 (2-3) (2012)