Foundations and trends in machine learning

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A unifying tutorial on approximate message passing

Oliver Y. Feng ... [et al.]

now Publishers c2022 Foundations and trends in machine learning v. 15, issue 4

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Computational optimal transport : with applications to data sciences

Gabriel Peyré, Marco Cuturi

now Publishers c2019 Foundations and trends in machine learning v. 11, issue 5-6

: pbk

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An introduction to Wishart matrix moments

Adrian N. Bishop, Pierre del Moral, Angèle Niclas

now Publishers c2018 Foundations and trends in machine learning v. 11, issue 2

: pbk

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4

An introduction to deep reinforcement learning

Vincent François-Lavet ... [et al.]

now Publishers c2018 Foundations and trends in machine learning v. 11, issue 3-4

: pbk

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Applications and future perspectives

Andrzej Cichocki ... [et al.]

now Publishers c2017 Foundations and trends in machine learning 9:6 . Tensor networks for dimensionality reduction and large-scale optimization ; part 2

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6

Low-rank tensor decompositions

Andrzej Cichocki ... [et al.]

now Publishers Foundations and trends in machine learning 9:4-5 . Tensor networks for dimensionality reduction and large-scale optimization ; part 1

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Non-convex optimization for machine learning

Prateek Jain, Purushottam Kar

now Publishers c2017 Foundations and trends in machine learning v. 10, issue 3-4

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Kernel mean embedding of distributions: a review and beyond

Krikamol Muandet ... [et al.]

now Publishers c2017 Foundations and trends in machine learning v. 10, issue 1-2

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Convex optimization : algorithms and complexity

Sébastien Bubeck

now Publishers c2015 Foundations and trends in machine learning v. 8, issue 3-4

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Bayesian Reinforcement Learning: A Survey

Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar

now Publishers c2015 Foundations and trends in machine learning v. 8, issue 5-6

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An introduction to matrix concentration inequalities

Joel A. Tropp

now Publishers c2015 Foundations and trends in machine learning v. 8, issue 1-2

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From bandits to monte-carlo tree search : the optimistic principle applied to optimization and planning

Rèmi Munos

now Publishers c2014 Foundations and trends in machine learning v. 7, issue 1

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

Alborz Geramifard, Thomas J. Walsh, Stefanie Tellex, Girish Chowdhary, Nicholas Roy, Jonathan P. How

now Publishers c2013 Foundations and trends in machine learning v. 6, issue 4

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Learning with submodular functions : a convex optimization perspective

Francis Bach

now Publishers c2013 Foundations and trends in machine learning v. 6, issue 2-3

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Determinantal point processes for machine learning

Alex Kulesza, Ben Taskar

now Publishers c2012 Foundations and trends in machine learning v. 5, issue 2-3

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Regret analysis of stochastic and nonstochastic multi-armed bandit problems

Sébastien Bubeck, Nicolò Cesa-Bianchi

now Publishers c2012 Foundations and trends in machine learning v. 5, issue 1

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Kernels for vector-valued functions : a review

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

now Publishers c2012 Foundations and trends in machine learning v. 4, issue 3

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A survey of statistical network models

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

now Publishers [2009] Foundations and trends in machine learning v. 2, issue 2

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Learning deep architectures for AI

Yoshua Bengio

now Publishers c2009 Foundations and trends in machine learning v. 2, issue 1

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Learning representation and control in Markov decision processes : new frontiers

Sridhar Mahadevan

now Publishers [2008] Foundations and trends in machine learning v. 1, issue 4

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Foundations and trends in machine learning

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