Visualizing and understanding Sum-Product Networks
Improved linear embeddings via Lagrange duality
Continuation methods for approximate large scale object sequencing
The risk of trivial solutions in bipartite top ranking
Unsupervised feature selection based on kernel fisher discriminant analysis and regression learning
Corruption-tolerant bandit learning
2D compressed learning: support matrix machine with bilinear random projections
A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data
Speculate-correct error bounds for k-nearest neighbor classifiers
The kernel Kalman rule
Covariance-based dissimilarity measures applied to clustering wide-sense stationary ergodic processes
A greedy feature selection algorithm for Big Data of high dimensionality
Learning rates for kernel-based expectile regression
A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data
Fast generalization rates for distance metric learning
Extreme value correction: a method for correcting optimistic estimations in rule learning
Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms
Correction to: Modeling outcomes of soccer matches
Guest editorial: special issue on machine learning for soccer
The Open International Soccer Database for machine learning
Learning to predict soccer results from relational data with gradient boosted trees
Dolores: a model that predicts football match outcomes from all over the world
Modeling outcomes of soccer matches
Incorporating domain knowledge in machine learning for soccer outcome prediction
Probabilistic movement models and zones of control
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018
Guest editors’ note
Learning efficient logic programs
Semi-supervised online structure learning for composite event recognition
Lifted discriminative learning of probabilistic logic programs
Probabilistic and exact frequent subtree mining in graphs beyond forests
Online probabilistic theory revision from examples with ProPPR
Algorithms for learning parsimonious context trees
Arbitrage of forecasting experts
Constructing effective personalized policies using counterfactual inference from biased data sets with many features
Accelerated gradient boosting
Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion
A simple homotopy proximal mapping algorithm for compressive sensing
Conformal and probabilistic prediction with applications: editorial
Rethinking statistical learning theory: learning using statistical invariants
Online aggregation of unbounded losses using shifting experts with confidence
Nonparametric predictive distributions based on conformal prediction
Majority vote ensembles of conformal predictors
Combination of inductive mondrian conformal predictors
Automatic face recognition with well-calibrated confidence measures
Efficient Venn predictors using random forests
Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018)
Good arm identification via bandit feedback
Supervised representation learning for multi-label classification
Bayesian optimistic Kullback–Leibler exploration
Annotation cost-sensitive active learning by tree sampling
N-ary decomposition for multi-class classification
Millionaire: a hint-guided approach for crowdsourcing
An accelerated variance reducing stochastic method with Douglas-Rachford splitting
Engineering fast multilevel support vector machines
Asymptotically optimal algorithms for budgeted multiple play bandits
Boosting as a kernel-based method
Risk bound of transfer learning using parametric feature mapping and its application to sparse coding
A distributed feature selection scheme with partial information sharing
RankMerging: a supervised learning-to-rank framework to predict links in large social networks
Attentional multilabel learning over graphs: a message passing approach
A Riemannian gossip approach to subspace learning on Grassmann manifold
Distributed Bayesian matrix factorization with limited communication
Collaborative topic regression for predicting topic-based social influence
Dynamic attention-integrated neural network for session-based news recommendation
Correction to: Adaptive random forests for evolving data stream classification
Introduction to the special issue for the ECML PKDD 2019 journal track
Dynamic principal projection for cost-sensitive online multi-label classification
Aggregating Algorithm for prediction of packs
Efficient feature selection using shrinkage estimators
Grouped Gaussian processes for solar power prediction
LSALSA: accelerated source separation via learned sparse coding
Data scarcity, robustness and extreme multi-label classification
Joint detection of malicious domains and infected clients
A flexible probabilistic framework for large-margin mixture of experts
Deep collective matrix factorization for augmented multi-view learning
Temporal pattern attention for multivariate time series forecasting
Compatible natural gradient policy search
TD-regularized actor-critic methods
On PAC-Bayesian bounds for random forests
Efficient learning with robust gradient descent
Nuclear discrepancy for single-shot batch active learning
Improving latent variable descriptiveness by modelling rather than ad-hoc factors
CaDET: interpretable parametric conditional density estimation with decision trees and forests
On the analysis of adaptability in multi-source domain adaptation
The teaching size: computable teachers and learners for universal languages
Distribution-free uncertainty quantification for kernel methods by gradient perturbations
Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference
