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From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč +2
There are various measures of predictive uncertainty in the literature, but their relationships to each other remain unclear. This paper uses a decomposition of statistical pointwi…
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks
Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar +2
In modern federated learning, one of the main challenges is to account for inherent heterogeneity and the diverse nature of data distributions for different clients. This problem i…
Efficient Conformal Prediction under Data Heterogeneity
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii +7
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods…