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Machine Learning, Volume 108
Volume 108, Number 1, January 2019
- Daniel Berrar
, Philippe Lopes
Guest editorial: special issue on machine learning for soccer. 1-7 - Werner Dubitzky, Philippe Lopes
The Open International Soccer Database for machine learning. 9-28 - Ondrej Hubácek
Learning to predict soccer results from relational data with gradient boosted trees. 29-47 - Anthony C. Constantinou
Dolores: a model that predicts football match outcomes from all over the world. 49-75 - Alkeos Tsokos
Modeling outcomes of soccer matches. 77-95 - Daniel Berrar, Philippe Lopes
Incorporating domain knowledge in machine learning for soccer outcome prediction. 97-126 - Ulf Brefeld, Jan Lasek
Probabilistic movement models and zones of control. 127-147
Volume 108, Number 2, February 2019
- Ioannis Tsamardinos
A greedy feature selection algorithm for Big Data of high dimensionality. 149-202 - Muhammad Farooq, Ingo Steinwart:
Learning rates for kernel-based expectile regression. 203-227 - Marc Boullé, Clément Charnay, Nicolas Lachiche
A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data. 229-266 - Han-Jia Ye, De-Chuan Zhan
Fast generalization rates for distance metric learning. 267-295 - Martin Mozina, Janez Demsar, Ivan Bratko, Jure Zabkar:
Extreme value correction: a method for correcting optimistic estimations in rule learning. 297-329 - Kai Ming Ting, Ye Zhu
Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms. 331-376 - Alkeos Tsokos
Correction to: Modeling outcomes of soccer matches. 377-378
Volume 108, Number 3, March 2019
- Alexander Gammerman
Conformal and probabilistic prediction with applications: editorial. 379-380 - Vladimir Vapnik, Rauf Izmailov
Rethinking statistical learning theory: learning using statistical invariants. 381-423 - Vladimir V'yugin
Online aggregation of unbounded losses using shifting experts with confidence. 425-444 - Vladimir Vovk
Nonparametric predictive distributions based on conformal prediction. 445-474 - Giovanni Cherubin
Majority vote ensembles of conformal predictors. 475-488 - Paolo Toccaceli
Combination of inductive mondrian conformal predictors. 489-510 - Charalambos Eliades, Ladislav Lenc, Pavel Král
Automatic face recognition with well-calibrated confidence measures. 511-534 - Ulf Johansson
Efficient Venn predictors using random forests. 535-550
Volume 108, Number 4, April 2019
- Antonio Vergari
Visualizing and understanding Sum-Product Networks. 551-573 - Kshiteej Sheth, Dinesh Garg, Anirban Dasgupta
Improved linear embeddings via Lagrange duality. 575-594 - Xenophon Evangelopoulos, Austin J. Brockmeier
Continuation methods for approximate large scale object sequencing. 595-626 - Aditya Krishna Menon:
The risk of trivial solutions in bipartite top ranking. 627-658 - Ronghua Shang, Yang Meng
Unsupervised feature selection based on kernel fisher discriminant analysis and regression learning. 659-686 - Sayash Kapoor
Corruption-tolerant bandit learning. 687-715
Volume 108, Number 5, May 2019
- Masashi Sugiyama
Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018). 717-719 - Hideaki Kano
Good arm identification via bandit feedback. 721-745 - Ming Huang, Fuzhen Zhuang
Supervised representation learning for multi-label classification. 747-763 - Kanghoon Lee, Geon-hyeong Kim
Bayesian optimistic Kullback-Leibler exploration. 765-783 - Yu-Lin Tsou
Annotation cost-sensitive active learning by tree sampling. 785-807 - Joey Tianyi Zhou
N-ary decomposition for multi-class classification. 809-830 - Bo Han
Millionaire: a hint-guided approach for crowdsourcing. 831-858 - Jingchang Liu, Linli Xu, Shuheng Shen, Qing Ling:
An accelerated variance reducing stochastic method with Douglas-Rachford splitting. 859-878
Volume 108, Number 6, June 2019
- Ralf Eggeling
Algorithms for learning parsimonious context trees. 879-911 - Vítor Cerqueira
Arbitrage of forecasting experts. 913-944 - Onur Atan, William R. Zame, Qiaojun Feng
Constructing effective personalized policies using counterfactual inference from biased data sets with many features. 945-970 - Gérard Biau
Accelerated gradient boosting. 971-992 - He Yan, Qiaolin Ye, Dong-Jun Yu:
Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion. 993-1018 - Tianbao Yang
A simple homotopy proximal mapping algorithm for compressive sensing. 1019-1056
Volume 108, Number 7, July 2019
- Nicolas Lachiche
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018. 1057-1059 - Guest editors' note. 1061
- Andrew Cropper, Stephen H. Muggleton:
Learning efficient logic programs. 1063-1083 - Evangelos Michelioudakis
Semi-supervised online structure learning for composite event recognition. 1085-1110 - Arnaud Nguembang Fadja
Lifted discriminative learning of probabilistic logic programs. 1111-1135 - Pascal Welke
Probabilistic and exact frequent subtree mining in graphs beyond forests. 1137-1164 - Victor Guimarães, Aline Paes
Online probabilistic theory revision from examples with ProPPR. 1165-1189
Volume 108, Numbers 8-9, September 2019
- Karsten M. Borgwardt, Po-Ling Loh
Introduction to the special issue for the ECML PKDD 2019 journal track. 1191-1192 - Hong-Min Chu, Kuan-Hao Huang
Dynamic principal projection for cost-sensitive online multi-label classification. 1193-1230 - Dmitry Adamskiy, Anthony Bellotti, Raisa Dzhamtyrova, Yuri Kalnishkan
Aggregating Algorithm for prediction of packs. 1231-1260 - Konstantinos Sechidis
Efficient feature selection using shrinkage estimators. 1261-1286 - Astrid Dahl
Grouped Gaussian processes for solar power prediction. 1287-1306 - Benjamin Cowen
LSALSA: accelerated source separation via learned sparse coding. 1307-1327 - Rohit Babbar
Data scarcity, robustness and extreme multi-label classification. 1329-1351 - Paul Prasse
Joint detection of malicious domains and infected clients. 1353-1368 - Archit Sharma, Siddhartha Saxena
A flexible probabilistic framework for large-margin mixture of experts. 1369-1393 - Ragunathan Mariappan, Vaibhav Rajan
Deep collective matrix factorization for augmented multi-view learning. 1395-1420 - Shun-Yao Shih
Temporal pattern attention for multivariate time series forecasting. 1421-1441 - Joni Pajarinen
Compatible natural gradient policy search. 1443-1466 - Simone Parisi
TD-regularized actor-critic methods. 1467-1501 - Stephan Sloth Lorenzen
On PAC-Bayesian bounds for random forests. 1503-1522 - Matthew J. Holland, Kazushi Ikeda:
Efficient learning with robust gradient descent. 1523-1560 - Tom J. Viering
Nuclear discrepancy for single-shot batch active learning. 1561-1599 - Alex Mansbridge
Improving latent variable descriptiveness by modelling rather than ad-hoc factors. 1601-1611 - Cyrus Cousins, Matteo Riondato
CaDET: interpretable parametric conditional density estimation with decision trees and forests. 1613-1634 - Ievgen Redko
On the analysis of adaptability in multi-source domain adaptation. 1635-1652 - Jan Arne Telle, José Hernández-Orallo, Cèsar Ferri
The teaching size: computable teachers and learners for universal languages. 1653-1675 - Balázs Csanád Csáji
Distribution-free uncertainty quantification for kernel methods by gradient perturbations. 1677-1699 - Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, Jian Li:
Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference. 1701-1727
Volume 108, Number 10, October 2019
- Lionel Tabourier, Daniel Faria Bernardes, Anne-Sophie Libert
RankMerging: a supervised learning-to-rank framework to predict links in large social networks. 1729-1756 - Kien Do
Attentional multilabel learning over graphs: a message passing approach. 1757-1781 - Bamdev Mishra
A Riemannian gossip approach to subspace learning on Grassmann manifold. 1783-1803 - Xiangju Qin
Distributed Bayesian matrix factorization with limited communication. 1805-1830 - Asso Hamzehei
Collaborative topic regression for predicting topic-based social influence. 1831-1850 - Lemei Zhang
Dynamic attention-integrated neural network for session-based news recommendation. 1851-1875 - Heitor Murilo Gomes
Correction to: Adaptive random forests for evolving data stream classification. 1877-1878
Volume 108, Number 11, November 2019
- Ehsan Sadrfaridpour
Engineering fast multilevel support vector machines. 1879-1917 - Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz
Asymptotically optimal algorithms for budgeted multiple play bandits. 1919-1949 - Aleksandr Y. Aravkin
Boosting as a kernel-based method. 1951-1974 - Wataru Kumagai
Risk bound of transfer learning using parametric feature mapping and its application to sparse coding. 1975-2008 - Aida Brankovic
A distributed feature selection scheme with partial information sharing. 2009-2034
Volume 108, Number 12, December 2019
- Di Ma, Songcan Chen:
2D compressed learning: support matrix machine with bilinear random projections. 2035-2060 - Kshitij Khare, Sang-Yun Oh
A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data. 2061-2086 - Eric Bax, Lingjie Weng, Xu Tian:
Speculate-correct error bounds for k-nearest neighbor classifiers. 2087-2111 - Gregor H. W. Gebhardt
The kernel Kalman rule - Efficient nonparametric inference by recursive least-squares and subspace projections. 2113-2157 - Qidi Peng
Covariance-based dissimilarity measures applied to clustering wide-sense stationary ergodic processes. 2159-2195
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