- 2018
- Adrian N. Bishop, Pierre Del Moral, Angèle Niclas:
An Introduction to Wishart Matrix Moments. Found. Trends Mach. Learn. 11(2): 97-218 (2018) - George H. Chen, Devavrat Shah:
Explaining the Success of Nearest Neighbor Methods in Prediction. Found. Trends Mach. Learn. 10(5-6): 337-588 (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): 219-354 (2018) - Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen:
A Tutorial on Thompson Sampling. Found. Trends Mach. Learn. 11(1): 1-96 (2018) - 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): 431-673 (2017) - Prateek Jain, Purushottam Kar:
Non-convex Optimization for Machine Learning. Found. Trends Mach. Learn. 10(3-4): 142-336 (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): 1-141 (2017) - 2016
- Elaine Angelino, Matthew James Johnson, Ryan P. Adams:
Patterns of Scalable Bayesian Inference. Found. Trends Mach. Learn. 9(2-3): 119-247 (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): 249-429 (2016) - Madeleine Udell, Corinne Horn, Reza Zadeh, Stephen P. Boyd:
Generalized Low Rank Models. Found. Trends Mach. Learn. 9(1): 1-118 (2016) - 2015
- Sébastien Bubeck:
Convex Optimization: Algorithms and Complexity. Found. Trends Mach. Learn. 8(3-4): 231-357 (2015) - Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar:
Bayesian Reinforcement Learning: A Survey. Found. Trends Mach. Learn. 8(5-6): 359-483 (2015) - Joel A. Tropp:
An Introduction to Matrix Concentration Inequalities. Found. Trends Mach. Learn. 8(1-2): 1-230 (2015) - 2014
- Silvia Chiappa:
Explicit-Duration Markov Switching Models. Found. Trends Mach. Learn. 7(6): 803-886 (2014) - Steve Hanneke:
Theory of Disagreement-Based Active Learning. Found. Trends Mach. Learn. 7(2-3): 131-309 (2014) - Rémi Munos:
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning. Found. Trends Mach. Learn. 7(1): 1-129 (2014) - Ali H. Sayed:
Adaptation, Learning, and Optimization over Networks. Found. Trends Mach. Learn. 7(4-5): 311-801 (2014) - 2013
- Francis R. Bach:
Learning with Submodular Functions: A Convex Optimization Perspective. Found. Trends Mach. Learn. 6(2-3): 145-373 (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): 375-451 (2013) - Brian Kulis:
Metric Learning: A Survey. Found. Trends Mach. Learn. 5(4): 287-364 (2013) - Fredrik Lindsten, Thomas B. Schön:
Backward Simulation Methods for Monte Carlo Statistical Inference. Found. Trends Mach. Learn. 6(1): 1-143 (2013) - 2012
- Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence:
Kernels for Vector-Valued Functions: A Review. Found. Trends Mach. Learn. 4(3): 195-266 (2012) - Francis R. Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski:
Optimization with Sparsity-Inducing Penalties. Found. Trends Mach. Learn. 4(1): 1-106 (2012) - Sébastien Bubeck, Nicolò Cesa-Bianchi:
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Found. Trends Mach. Learn. 5(1): 1-122 (2012) - Alex Kulesza, Ben Taskar:
Determinantal Point Processes for Machine Learning. Found. Trends Mach. Learn. 5(2-3): 123-286 (2012) - Pierre Del Moral, Peng Hu, Liming Wu:
On the Concentration Properties of Interacting Particle Processes. Found. Trends Mach. Learn. 3(3-4): 225-389 (2012) - Shai Shalev-Shwartz:
Online Learning and Online Convex Optimization. Found. Trends Mach. Learn. 4(2): 107-194 (2012) - Charles Sutton, Andrew McCallum:
An Introduction to Conditional Random Fields. Found. Trends Mach. Learn. 4(4): 267-373 (2012) - 2011
- Stephen P. Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein:
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Found. Trends Mach. Learn. 3(1): 1-122 (2011) - Michael W. Mahoney:
Randomized Algorithms for Matrices and Data. Found. Trends Mach. Learn. 3(2): 123-224 (2011)