Foreword: special issue for the journal track of the 9th Asian Conference on Machine Learning (ACML 2017)
Efficient preconditioning for noisy separable nonnegative matrix factorization problems by successive projection based low-rank approximations
Robust Plackett–Luce model for k-ary crowdsourced preferences
Learning safe multi-label prediction for weakly labeled data
Distributed multi-task classification: a decentralized online learning approach
Crowdsourcing with unsure option
Semi-supervised AUC optimization based on positive-unlabeled learning
Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning
Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation
Wasserstein discriminant analysis
Stochastic variational hierarchical mixture of sparse Gaussian processes for regression
Clustering with missing features: a penalized dissimilarity measure based approach
An adaptive heuristic for feature selection based on complementarity
LPiTrack: Eye movement pattern recognition algorithm and application to biometric identification
Learning data discretization via convex optimization
Simple strategies for semi-supervised feature selection
When is the Naive Bayes approximation not so naive?
Emotion in reinforcement learning agents and robots: a survey
Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue
Efficient benchmarking of algorithm configurators via model-based surrogates
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization
Speeding up algorithm selection using average ranking and active testing by introducing runtime
Instance spaces for machine learning classification
The online performance estimation framework: heterogeneous ensemble learning for data streams
Discovering predictive ensembles for transfer learning and meta-learning
Data complexity meta-features for regression problems
Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
Preface to the special issue on inductive logic programming
Meta-Interpretive Learning from noisy images
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP
Best-effort inductive logic programming via fine-grained cost-based hypothesis generation
Identification of biological transition systems using meta-interpreted logic programs
On better training the infinite restricted Boltzmann machines
An incremental off-policy search in a model-free Markov decision process using a single sample path
Wallenius Bayes
A scalable preference model for autonomous decision-making
Improved maximum inner product search with better theoretical guarantee using randomized partition trees
Analysis of classifiers’ robustness to adversarial perturbations
The randomized information coefficient: assessing dependencies in noisy data
Identifying and tracking topic-level influencers in the microblog streams
1-Bit matrix completion: PAC-Bayesian analysis of a variational approximation
Manifold-based synthetic oversampling with manifold conformance estimation
Learning with rationales for document classification
Consensus-based modeling using distributed feature construction with ILP
Online multi-label dependency topic models for text classification
Simpler PAC-Bayesian bounds for hostile data
Dyad ranking using Plackett–Luce models based on joint feature representations
Introduction to the special issue on discovery science
A comparison of hierarchical multi-output recognition approaches for anuran classification
Ensembles for multi-target regression with random output selections
Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams
On analyzing user preference dynamics with temporal social networks
Discovering a taste for the unusual: exceptional models for preference mining
Targeted and contextual redescription set exploration
Probabilistic frequent subtrees for efficient graph classification and retrieval
Analyzing business process anomalies using autoencoders
Guest editors introduction to the special issue for the ECML PKDD 2018 journal track
Approximate structure learning for large Bayesian networks
Output Fisher embedding regression
Global multi-output decision trees for interaction prediction
High-dimensional penalty selection via minimum description length principle
Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes
Stagewise learning for noisy k-ary preferences
Deep Gaussian Process autoencoders for novelty detection
An online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method
A new method of moments for latent variable models
A distributed Frank–Wolfe framework for learning low-rank matrices with the trace norm
Similarity encoding for learning with dirty categorical variables
ML-Plan: Automated machine learning via hierarchical planning
Inverse reinforcement learning from summary data
On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis
Learning from binary labels with instance-dependent noise
Optimizing non-decomposable measures with deep networks
Local contrast as an effective means to robust clustering against varying densities
