Elements of an Automatic Data Scientist.- The Need for Interpretability Biases Open Data Science.- Automatic POI Matching Using an Outlier Detection Based Approach.- Fact Checking from Natural Text with Probabilistic Soft Logic.- ConvoMap: Using Convolution to Order Boolean Data.- Training Neural Networks to distinguish craving smokers, non-craving smokers, and non-smokers.- Missing Data Imputation via Denoising Autoencoders: the untold story.- Online Non-Linear Gradient Boosting in Multi-Latent Spaces.- MDP-based Itinerary Recommendation using Geo-Tagged Social Media.- Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization.- Non-Negative Local Sparse Coding for Subspace Clustering.- Pushing the Envelope in Overlapping Communities Detection.-Right for the Right Reason: Training Agnostic Networks.- Link Prediction in Multi-Layer Networks and its Application to Drug Design.- A hierarchical Ornstein-Uhlenbeck model for stochastic time series analysis.- Analysing the footprint of classi_ers in overlapped and imbalanced contexts.- Tree-based Cost Sensitive Methods for Fraud Detection in Imbalanced Data.- Reduction Stumps for Multi-Class Classification.- Decomposition of quantitative Gaifman graphs as a data analysis tool.- Exploring the Effects of Data Distribution in Missing Data Imputation.- Communication-free Widened Learning of Bayesian Network Classifiers Using Hashed Fiedler Vectors.- Expert finding in Citizen Science platform for biodiversity monitoring via weighted PageRank algorithm.- Random forests with latent variables to foster feature selection in the context of highly correlated variables. Illustration with a bioinformatics application.-Don't Rule Out Simple Models Prematurely: a Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML.- Detecting Shifts in Public Opinion: a big data study of global news content.- Biased Embeddings from Wild Data: Measuring, Understanding and Removing.- Real-Time Excavation Detection at Construction Sites using Deep Learning.- COBRAS: Interactive Clustering with Pairwise Queries.- Automatically Wrangling Spreadsheets into Machine Learning Data Formats.- Learned Feature Generation for Molecules.