Titlebook: Discovery Science; 23rd International C Annalisa Appice,Grigorios Tsoumakas,Stan Matwin Conference proceedings 2020 Springer Nature Switzer

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Claudia Hub,Verena Groer,Brigitte Wagenhalse of automatically processing tens of thousands of scientific publications with the aim to enrich existing empirical evidence with literature-based associations is challenging and relevant. We propose a system for contextualization of empirical expression data by approximating relations between enti

Stagger

Ralph Schneider,Theresa SchwarzkopfoExp, an OntoDM module which gives a more granular representation of a predictive modeling experiment and enables annotation of the experiment’s provenance, algorithm implementations, parameter settings and output metrics. This module is incorporated in SemanticHub, an online system that allows exec

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analogous

Positionierung und Internationalisierung,ew instances. In such dynamic environments, in which the underlying data distributions might evolve with time, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore

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Studierendenmarketing und Hochschulbranding-making. Most of the proposed fairness-aware learning algorithms process the data in offline settings and assume that the data is generated by a single concept without drift. Unfortunately, in many real-world applications, data is generated in a streaming fashion and can only be scanned once. In add

judiciousness

Forschungsfrage(n) und Methodologie,m monitor the loss of predictive models. Accordingly, an alarm is launched when the loss increases significantly, which triggers some adaptation mechanism (e.g. retrain the model). However, this . falls short in many real-world scenarios, where the true labels are not readily available to compute th

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mediocrity

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/281055.jpg

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Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approachy encountered in such settings and prevent a direct evaluation. First, the data may not have included all factors that affected past decisions. And second, past decisions may have led to unobserved outcomes. This is the case, for example, when a bank decides whether a customer should be granted a lo

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