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Article
Open AccessContext discovery for anomaly detection
Contextual anomaly detection aims to identify objects that are anomalous only within specific contexts, while appearing normal otherwise. However, most existing methods are limited to a single context defined ...
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Article
Open AccessWisdom of the contexts: active ensemble learning for contextual anomaly detection
In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods use a single context based on a set of user-specified contextual features. However, iden...
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Chapter and Conference Paper
Interactive Anomaly Detection Based on Clustering and Online Mirror Descent
In several applications, when anomalies are detected, human experts have to investigate or verify them one by one. As they investigate, they unwittingly produce a label - true positive (TP) or false positive (...
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Article
Open AccessEfficient differentially private learning improves drug sensitivity prediction
Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if other users are willing to share their private information. Good personalised pr...
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Chapter and Conference Paper
Conjugate Gamma Markov Random Fields for Modelling Nonstationary Sources
In modelling nonstationary sources, one possible strategy is to define a latent process of strictly positive variables to model variations in second order statistics of the underlying process. This can be achi...
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Chapter and Conference Paper
Estimating Distributions in Genetic Algorithms
The canonical operators of genetic algorithms, i.e., mutation and crossover, have nondeterministic effects on the population.They use information from only one or two fit individuals and risk deforming the chr...
