Special Issue of the ECML PKDD 2021 Journal Track

Guest Editors: Annalisa Appice, Sergio Escalera, Jose A. Gamez, Heike Trautmann

Journal

Machine Learning

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

Annalisa Appice

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Sergio Escalera

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Jose A. Gamez

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Heike Trautmann

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Showing 1-23 of 23 articles

Robustness verification of ReLU networks via quadratic programming
Aleksei Kuvshinov
Stephan Günnemann
OriginalPaper Open access 16 March 2022 Pages: 2407 - 2433
Introduction to the special issue of the ECML PKDD 2021 journal track
Annalisa Appice
Sergio Escalera
Heike Trautmann
EditorialNotes 27 September 2021 Pages: 2991 - 2992
Policy space identification in configurable environments
Alberto Maria Metelli
Guglielmo Manneschi
Marcello Restelli
OriginalPaper Open access 05 September 2021 Pages: 2093 - 2145
Robust non-parametric regression via incoherent subspace projections
Bhaskar Mukhoty
Subhajit Dutta
Purushottam Kar
OriginalPaper 05 September 2021 Pages: 2941 - 2989
Provable training set debugging for linear regression
Xiaomin Zhang
Xiaojin Zhu
Po-Ling Loh
OriginalPaper 16 August 2021 Pages: 2763 - 2834
Testing conditional independence in supervised learning algorithms
David S. Watson
Marvin N. Wright
OriginalPaper Open access 02 August 2021 Pages: 2107 - 2129
Sampled Gromov Wasserstein
Tanguy Kerdoncuff
Rémi Emonet
Marc Sebban
OriginalPaper 26 July 2021 Pages: 2151 - 2186
Convex optimization with an interpolation-based projection and its application to deep learning
Riad Akrour
Asma Atamna
Jan Peters
OriginalPaper Open access 19 July 2021 Pages: 2267 - 2289
Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting
Kai Chen
Twan van Laarhoven
Elena Marchiori
OriginalPaper Open access 12 July 2021 Pages: 2213 - 2238
On testing transitivity in online preference learning
Björn Haddenhorst
Viktor Bengs
Eyke Hüllermeier
OriginalPaper Open access 12 July 2021 Pages: 2063 - 2084
Variational learning from implicit bandit feedback
Quoc-Tuan Truong
Hady W. Lauw
OriginalPaper 09 July 2021 Pages: 2085 - 2105
AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow
Haiyan Jiang
Haoyi Xiong
Dejing Dou
S.I. : ECML PKDD 2021 07 July 2021 Pages: 2131 - 2150
Information-theoretic regularization for learning global features by sequential VAE
Kei Akuzawa
Yusuke Iwasawa
Yutaka Matsuo
OriginalPaper Open access 07 July 2021 Pages: 2239 - 2266
Density-based weighting for imbalanced regression
Michael Steininger
Konstantin Kobs
Andreas Hotho
OriginalPaper Open access 07 July 2021 Pages: 2187 - 2211
Joint optimization of an autoencoder for clustering and embedding
Ahcène Boubekki
Michael Kampffmeyer
Robert Jenssen
OriginalPaper Open access 21 June 2021 Pages: 1901 - 1937
Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels
Dai Hai Nguyen
Canh Hao Nguyen
Hiroshi Mamitsuka
OriginalPaper 13 June 2021 Pages: 1585 - 1607
MODES: model-based optimization on distributed embedded systems
Junjie Shi
Jiang Bian
Jian-Jia Chen
OriginalPaper Open access 04 June 2021 Pages: 1527 - 1547
Multiple clusterings of heterogeneous information networks
Shaowei Wei
Guoxian Yu
Xiangliang Zhang
OriginalPaper 02 June 2021 Pages: 1505 - 1526
Toward optimal probabilistic active learning using a Bayesian approach
Daniel Kottke
Marek Herde
Bernhard Sick
OriginalPaper Open access 04 May 2021 Pages: 1199 - 1231
Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training
Anna-Kathrin Kopetzki
Stephan Günnemann
OriginalPaper Open access 28 April 2021 Pages: 1175 - 1197
Protect privacy of deep classification networks by exploiting their generative power
Jiyu Chen
Yiwen Guo
Hao Chen
OriginalPaper Open access 13 April 2021 Pages: 651 - 674
SPEED: secure, PrivatE, and efficient deep learning
Arnaud Grivet Sébert
Rafaël Pinot
Renaud Sirdey
OriginalPaper 23 March 2021 Pages: 675 - 694
Bayesian optimization with approximate set kernels
Jungtaek Kim
Michael McCourt
Seungjin Choi
OriginalPaper 22 March 2021 Pages: 857 - 879

Special Issue of the ECML PKDD 2021 Journal Track

Participating journal

Machine Learning

Editors

Annalisa Appice

Sergio Escalera

Jose A. Gamez

Heike Trautmann

Articles

Robustness verification of ReLU networks via quadratic programming

Introduction to the special issue of the ECML PKDD 2021 journal track

Policy space identification in configurable environments

Robust non-parametric regression via incoherent subspace projections

Provable training set debugging for linear regression

Testing conditional independence in supervised learning algorithms

Sampled Gromov Wasserstein

Convex optimization with an interpolation-based projection and its application to deep learning

Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting

On testing transitivity in online preference learning

Variational learning from implicit bandit feedback

AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow

Information-theoretic regularization for learning global features by sequential VAE

Density-based weighting for imbalanced regression

Joint optimization of an autoencoder for clustering and embedding

Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels

MODES: model-based optimization on distributed embedded systems

Multiple clusterings of heterogeneous information networks

Toward optimal probabilistic active learning using a Bayesian approach

Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training

Protect privacy of deep classification networks by exploiting their generative power

SPEED: secure, PrivatE, and efficient deep learning

Bayesian optimization with approximate set kernels

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Special Issue of the ECML PKDD 2021 Journal Track

Participating journal

Editors

Annalisa Appice

Sergio Escalera

Jose A. Gamez

Heike Trautmann

Articles

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