Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Frank Hutter,Kristian Kersting,Isabel Valera Conference proceed

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Efficiency of Coordinate Descent Methods for Structured Nonconvex Optimizationi) a simple convex term, and (iii) a concave and continuous term. First, by extending randomized CD to nonsmooth nonconvex settings, we develop a coordinate subgradient method that randomly updates block-coordinate variables by using block composite subgradient mapping. This method converges asympto

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Learning Gradient Boosted Multi-label Classification Rulesough different, loss functions have been proposed. Ideally, the learning algorithm should be customizable towards a specific choice of the performance measure. Modern implementations of boosting, most prominently gradient boosted decision trees, appear to be appealing from this point of view. Howeve

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Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boostingproblem of kernel learning. Our study builds on a recent result showing that one can learn a distribution over the RFF to produce a new kernel suited for the task at hand. For learning this distribution, we exploit a GB scheme expressed as ensembles of RFF weak learners, each of them being a kernel

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Fairness by Explicability and Adversarial SHAP Learningto the deployment and adoption of machine learning systems. SHAP values provide a unified framework for interpreting model predictions and feature attribution but do not address the problem of fairness directly. In this work, we propose a new definition of fairness that emphasises the role of an ext

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