Introduction: special issue of selected papers from ACML 2015
Class-prior estimation for learning from positive and unlabeled data
Geometry-aware principal component analysis for symmetric positive definite matrices
Preference Relation-based Markov Random Fields for Recommender Systems
Erratum to: Preference Relation-based Markov Random Fields for Recommender Systems
Surrogate regret bounds for generalized classification performance metrics
Maximum margin partial label learning
Proximal average approximated incremental gradient descent for composite penalty regularized empirical risk minimization
Introduction to the special issue on dynamic networks and knowledge discovery
Scalable computational techniques for centrality metrics on temporally detailed social network
Exceptional contextual subgraph mining
Tiles: an online algorithm for community discovery in dynamic social networks
Special issue on inductive logic programming
Relational data factorization
Planning in hybrid relational MDPs
kProbLog: an algebraic Prolog for machine learning
Soft quantification in statistical relational learning
Fast rates by transferring from auxiliary hypotheses
On the use of stochastic local search techniques to revise first-order logic theories from examples
An empirical study of on-line models for relational data streams
Boosted multivariate trees for longitudinal data
Adaptive edge weighting for graph-based learning algorithms
Improving probabilistic inference in graphical models with determinism and cycles
Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors
Generalization bounds for non-stationary mixing processes
Optimal learning with Bernstein online aggregation
A family of admissible heuristics for A* to perform inference in probabilistic classifier chains
Feature-weighted clustering with inner product induced norm based dissimilarity measures: an optimization perspective
Projected estimators for robust semi-supervised classification
Homotopy continuation approaches for robust SV classification and regression
Optimal classification trees
The mechanism of additive composition
Special issue on discovery science
Multi-label classification via multi-target regression on data streams
Stream-based semi-supervised learning for recommender systems
Memory-adaptive high utility sequential pattern mining over data streams
Big Data: from collection to visualization
High-probability minimax probability machines
An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music
Confidence curves: an alternative to null hypothesis significance testing for the comparison of classifiers
QCC: a novel clustering algorithm based on Quasi-Cluster Centers
Nearest neighbors distance ratio open-set classifier
Hierarchical Dirichlet scaling process
Online optimization for max-norm regularization
Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016)
A unified probabilistic framework for robust manifold learning and embedding
Collaborative topic regression for online recommender systems: an online and Bayesian approach
Progressive random k-labelsets for cost-sensitive multi-label classification
Non-redundant multiple clustering by nonnegative matrix factorization
Multi-view kernel completion
Asymptotic properties of Turing’s formula in relative error
A Bayesian nonparametric model for multi-label learning
Statistical comparison of classifiers through Bayesian hierarchical modelling
A note on model selection for small sample regression
Introduction to the special issue dedicated to the Journal Track of ECML PKDD 2017
Learning deep kernels in the space of dot product polynomials
Gaussian conditional random fields extended for directed graphs
Efficient parameter learning of Bayesian network classifiers
Vine copulas for mixed data : multi-view clustering for mixed data beyond meta-Gaussian dependencies
Graph-based predictable feature analysis
A constrained \(\ell \)1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models
Varying-coefficient models for geospatial transfer learning
Learning constraints in spreadsheets and tabular data
Adaptive random forests for evolving data stream classification
Constraint-based clustering selection
An expressive dissimilarity measure for relational clustering using neighbourhood trees
Weightless neural networks for open set recognition
Offline reinforcement learning with task hierarchies
Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction
Sparse probit linear mixed model
Robust regression using biased objectives
Preserving differential privacy in convolutional deep belief networks
Generalized exploration in policy search
Cost-sensitive label embedding for multi-label classification
Group online adaptive learning
