Special Issue of the ECML PKDD 2020 Journal Track

Guest Editors: Ira Assent, Carlotta Domeniconi, Aristides Gionis, Eyke Hüllermeier

Journal

Machine Learning

Machine Learning is an international forum focusing on computational approaches to learning.

Ira Assent

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Carlotta Domeniconi

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Aristides Gionis

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Eyke Hüllermeier

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Showing 1-13 of 13 articles

Fast greedy \(\mathcal {C}\)-bound minimization with guarantees
Baptiste Bauvin
Cécile Capponi
François Laviolette
OriginalPaper Open access 23 September 2020 Pages: 1945 - 1986
High-dimensional Bayesian optimization using low-dimensional feature spaces
Riccardo Moriconi
Marc Peter Deisenroth
K. S. Sesh Kumar
OriginalPaper Open access 21 September 2020 Pages: 1925 - 1943
Introduction to the special issue of the ECML PKDD 2020 journal track
Ira Assent
Carlotta Domeniconi
Eyke Hüllermeier
EditorialNotes 08 September 2020 Pages: 1697 - 1698
Imbalanced regression and extreme value prediction
Rita P. Ribeiro
Nuno Moniz
OriginalPaper 04 September 2020 Pages: 1803 - 1835
Ada-boundary: accelerating DNN training via adaptive boundary batch selection
Hwanjun Song
Sundong Kim
Jae-Gil Lee
OriginalPaper 04 September 2020 Pages: 1837 - 1853
Skew Gaussian processes for classification
Alessio Benavoli
Dario Azzimonti
Dario Piga
OriginalPaper Open access 04 September 2020 Pages: 1877 - 1902
A decision-theoretic approach for model interpretability in Bayesian framework
Homayun Afrabandpey
Tomi Peltola
Samuel Kaski
OriginalPaper Open access 04 September 2020 Pages: 1855 - 1876
Weak approximation of transformed stochastic gradient MCMC
Soma Yokoi
Takuma Otsuka
Issei Sato
OriginalPaper 04 September 2020 Pages: 1903 - 1923
Learning representations from dendrograms
Morteza Haghir Chehreghani
Mostafa Haghir Chehreghani
OriginalPaper Open access 16 August 2020 Pages: 1779 - 1802
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition
Haw-Shiuan Chang
Shankar Vembu
Andrew McCallum
OriginalPaper 05 August 2020 Pages: 1749 - 1778
Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication
Emanuele Pesce
Giovanni Montana
OriginalPaper Open access 23 January 2020 Pages: 1727 - 1747
Combining Bayesian optimization and Lipschitz optimization
Mohamed Osama Ahmed
Sharan Vaswani
Mark Schmidt
OriginalPaper 16 September 2020 Pages: 79 - 102
Active deep Q-learning with demonstration
Si-An Chen
Voot Tangkaratt
Masashi Sugiyama
OriginalPaper 08 November 2019 Pages: 1699 - 1725

Special Issue of the ECML PKDD 2020 Journal Track

Participating journal

Machine Learning

Editors

Ira Assent

Carlotta Domeniconi

Aristides Gionis

Eyke Hüllermeier

Articles

Fast greedy \(\mathcal {C}\)-bound minimization with guarantees

High-dimensional Bayesian optimization using low-dimensional feature spaces

Introduction to the special issue of the ECML PKDD 2020 journal track

Imbalanced regression and extreme value prediction

Ada-boundary: accelerating DNN training via adaptive boundary batch selection

Skew Gaussian processes for classification

A decision-theoretic approach for model interpretability in Bayesian framework

Weak approximation of transformed stochastic gradient MCMC

Learning representations from dendrograms

Using error decay prediction to overcome practical issues of deep active learning for named entity recognition

Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication

Combining Bayesian optimization and Lipschitz optimization

Active deep Q-learning with demonstration