Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Annalisa Appice,Pedro Pereira Rodrigues,Carlos Soa Conference p

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Hyperparameter Search Space Pruning – A New Component for Sequential Model-Based Hyperparameter Optiy done on the current data set..Pruning as a new component for SMBO is an orthogonal contribution but nevertheless we compare it to surrogate models that learn across data sets and extensively investigate the impact of pruning with and without initialization for various state of the art surrogate mo

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Multi-Task Learning with Group-Specific Feature Space Sharinge descent, which employs a consensus-form Alternating Direction Method of Multipliers algorithm to optimize the Multiple Kernel Learning weights and, hence, to determine task affinities. Empirical evaluation on seven data sets exhibits a statistically significant improvement of our framework’s resul

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Superset Learning Based on Generalized Loss Minimizationng technique for the problem of label ranking, in which the output space consists of all permutations of a fixed set of items. The label ranking method thus obtained is compared to existing approaches tackling the same problem.

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capillaries

Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: Complexity Beyond ted for data mining utilizes the fact that a matrix product can be interpreted as a sum of rank-1 matrices. Then the factorization of a matrix becomes the task of finding a small number of rank-1 matrices, sum of which is a good representation of the original matrix. Seen this way, it becomes obviou

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Scalable Bayesian Non-negative Tensor Factorization for Massive Count Dataonline) for dealing with massive tensors. Our generative model can handle overdispersed counts as well as infer the rank of the decomposition. Moreover, leveraging a reparameterization of the Poisson distribution as a multinomial facilitates conjugacy in the model and enables simple and efficient Gi

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A Practical Approach to Reduce the Learning Bias Under Covariate Shiftand the target domains while the conditional distributions of the target Y given X are the same. A common technique to deal with this problem, called importance weighting, amounts to reweighting the training instances in order to make them resemble the test distribution. However this usually comes a

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Hyperparameter Search Space Pruning – A New Component for Sequential Model-Based Hyperparameter Optirs faster and even achieve better final performance. Sequential model-based optimization (SMBO) is the current state of the art framework for automatic hyperparameter optimization. Currently, it consists of three components: a surrogate model, an acquisition function and an initialization technique.