Titlebook: Conformal and Probabilistic Prediction with Applications; 5th International Sy Alexander Gammerman,Zhiyuan Luo,Vladimir Vovk Conference pro

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Kate Macdonald,Christoph SingerWe construct a universal prediction system in the spirit of Popper’s falsifiability and Kolmogorov complexity. This prediction system does not depend on any statistical assumptions, but under the IID assumption it dominates, although in a rather weak sense, conformal prediction.

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Conformal Predictors for Compound Activity Predictionresses some specific challenges of this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Re

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Conformal Prediction of Disruptions from Scratch: Application to an ITER Scenariodrogen and deuterium campaigns), a one-layer disruption predictor has been tested from scratch. The results show a relevant improvement where the success rate (rate of disruptions predicted correctly) increases and the false alarm rate (rate of non-disruptive discharges misclassified) decreases, usi

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Evaluation of a Variance-Based Nonconformity Measure for Regression Forestswith a k-nearest neighbor-based nonconformity measure, was shown to obtain state-of-the-art performance with respect to efficiency, i.e., average size of prediction regions. However, the use of the nearest-neighbor procedure not only requires that all training data have to be retained in conjunction

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Binary Relevance Multi-label Conformal Predictores are guaranteed to be valid under the assumption that the data used are identically and independently distributed (i.i.d.). In this work, we extend the CP framework for multi-label classification, where an instance can belong to multiple classes in parallel. Applications include image tagging, doc

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