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6 papers · 1 filter
Bayesian preference elicitation for decision support in multiobjective optimization
Felix Huber, Sebastian Rojas Gonzalez, Raul Astudillo
We present a novel approach to help decision-makers efficiently identify preferred solutions from the Pareto set of a multi-objective optimization problem. Our method uses a Bayesi…
Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations
Jack M. Buckingham, Sebastian Rojas Gonzalez, Juergen Branke
Multi-objective Bayesian optimization aims to find the Pareto front of trade-offs between a set of expensive objectives while collecting as few samples as possible. In some cases,…
Highly Scalable Tensor Factorization for Prediction of Drug-Protein Interaction Type
Adam Arany, Jaak Simm, Pooya Zakeri +5
The understanding of the type of inhibitory interaction plays an important role in drug design. Therefore, researchers are interested to know whether a drug has competitive or non-…
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
Rein Houthooft, Filip De Turck
Tackling pattern recognition problems in areas such as computer vision, bioinformatics, speech or text recognition is often done best by taking into account task-specific statistic…
Fast Prediction with SVM Models Containing RBF Kernels
Marc Claesen, Frank De Smet, Johan A. K. Suykens +1
We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner product…
A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models
Marc Claesen, Frank De Smet, Johan A. K. Suykens +1
We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task…