Titlebook: Machine Learning and Knowledge Discovery in Databases, Part III; European Conference, Dimitrios Gunopulos,Thomas Hofmann,Michalis Vazirg Co

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Preference Elicitation and Inverse Reinforcement Learningn. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent’s preferences, policy and optionally, the obtained reward sequence, from observations. We examine the relation of the resulting approach to other statistical met

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A Novel Framework for Locating Software Faults Using Latent Divergencest and is costly. Recent years have seen much progress in techniques for automated fault localization, specifically using program spectra – executions of failed and passed test runs provide a basis for isolating the faults. Despite the progress, fault localization in large programs remains a challeng

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Transfer Learning with Adaptive Regularizersrm that matches with the learning task at hand. If the necessary domain expertise is rare or hard to formalize, it may be difficult to find a good regularizer. On the other hand, if plenty of related or similar data is available, it is a natural approach to adjust the regularizer for the new learnin

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Multimodal Nonlinear Filtering Using Gauss-Hermite Quadratureas single Gaussian distributions. In nonlinear filtering problems the posterior state distribution can, however, take complex shapes and even become multimodal so that single Gaussians are no longer sufficient. A standard solution to this problem is to use a bank of independent filters that individu

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Active Supervised Domain Adaptationning in a target domain can leverage information from a different but related source domain. Our proposed framework, Active Learning Domain Adapted (.), uses source domain knowledge to transfer information that facilitates active learning in the target domain. We propose two variants of .: a batch B

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Efficiently Approximating Markov Tree Bagging for High-Dimensional Density Estimatione mixtures generally outperform a single Markov tree maximizing the data likelihood, but are far more expensive to compute. In this paper, we describe new algorithms for approximating such models, with the aim of .. More specifically, we propose to use a filtering step obtained as a by-product from

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