13 citations · 36 across the 8 of their papers we have counts for
9 papers
Inductive Venn-Abers and related regressors
Ivan Petej, Vladimir Vovk
Venn-Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they have been applicable only to binary classifi…
Protected Probabilistic Classification Library
Ivan Petej
This paper introduces a new Python package specifically designed to address calibration of probabilistic classifiers under dataset shift. The method is demonstrated in binary and m…
Protected probabilistic classification
Vladimir Vovk, Ivan Petej, Alex Gammerman
This paper proposes a way of protecting probabilistic prediction models against changes in the data distribution, concentrating on the case of classification and paying particular…
Retrain or not retrain: Conformal test martingales for change-point detection
Vladimir Vovk, Ivan Petej, Ilia Nouretdinov +3
We argue for supplementing the process of training a prediction algorithm by setting up a scheme for detecting the moment when the distribution of the data changes and the algorith…
Computationally efficient versions of conformal predictive distributions
Vladimir Vovk, Ivan Petej, Ilia Nouretdinov +2
Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather…
Conformal calibrators
Vladimir Vovk, Ivan Petej, Paolo Toccaceli +1
Most existing examples of full conformal predictive systems, split-conformal predictive systems, and cross-conformal predictive systems impose severe restrictions on the adaptation…
Large-scale probabilistic predictors with and without guarantees of validity
Vladimir Vovk, Ivan Petej, Valentina Fedorova
This paper studies theoretically and empirically a method of turning machine-learning algorithms into probabilistic predictors that automatically enjoys a property of validity (per…
From conformal to probabilistic prediction
Vladimir Vovk, Ivan Petej, Valentina Fedorova
This paper proposes a new method of probabilistic prediction, which is based on conformal prediction. The method is applied to the standard USPS data set and gives encouraging resu…
Venn-Abers predictors
Vladimir Vovk, Ivan Petej
This paper continues study, both theoretical and empirical, of the method of Venn prediction, concentrating on binary prediction problems. Venn predictors produce probability-type…