Machine Learning, Optimization, and Data Science

Clustering.- CoPASample: A Heuristics based Covariance Preserving Data Augmentation.- Active Matrix Completion for Algorithm Selection.- A Framework for Multi- delity Modeling in Global Optimization Approaches.- Performance Evaluation of Local Surrogate Models in Bilevel Optimization.- BowTie - a deep learning feedforward neural network for sentiment analysis.- To What Extent Can Text Classifiation Help with Making Inferences About Students' Understanding.- Combinatorial Learning in Traffic Management.- Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits.- Designing an Optimal and Resilient iBGP Overlay with extended ORRTD.- GRASP Heuristics for the Stochastic Weighted Graph Fragmentation Problem.- Uniformly Most-Reliable Graphs and Antiholes.- Merging Quality Estimation for Binary Decision Diagrams with Binary Classfi ers.- Directed Acyclic Graph Reconstruction Leveraging Prior Partial Ordering Information.- Learning Scale and Shift-Invariant Dictionary for Sparse Representation.- Robust kernelized Bayesian matrix factorization for video background/foreground separation.- Parameter Optimization of Polynomial Kernel SVM from miniCV.- Analysing the Over t of the auto-sklearn Automated Machine Learning Tool.- A New Baseline for Automated Hyper-Parameter Optimization.- Optimal trade-o between sample size and precision of supervision for the xed effects panel data model.- Restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City.- Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study.- A Chained Neural Network Model for Photovoltaic Power Forecast.- Trading-o Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels.- Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size.- Multi-Task Learning by Pareto Optimality Nicosia.- Vital prognosis of patients in intensivecare units using an Ensemble of Bayesian Classifiers.- On the role of hub and orphan genes in the diagnosis of breast invasive carcinoma.- Approximating Probabilistic Constraints for Surgery Scheduling using Neural Networks.- Determining Principal Component Cardinality through the Principle of Minimum Description Length.- Modelling chaotic time series using recursive deep self-organising neural networks.- On Tree-based Methods for Similarity Learning.- Active Learning Approach for Safe Process Parameter Tuning.- Federated Learning of Deep Neural Decision Forests.- Data Anonymization for Privacy aware Machine Learning.- Exploiting Similar Behavior of Users in a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications.- Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions.- Zero-Shot Fashion Products Clustering on Social Image Streams.- Treating Arti cial Neural Net Training as a Nonsmooth Global Optimization Problem.