Joshua Wendland — accepted AI-conference papers

2026

Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs

Joshua Wendland, Markel Zubia, Roman Andriushchenko, Maris F. L. Galesloot, Milan Češka, Henrik von Kleist +3

IJCAI 2026

We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of missing data. A miss-MDP is a POMDP whose observation function is a missingness function, specifying the probability that individual state features a

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2025

Feature Importance Metrics in the Presence of Missing Data

Henrik von Kleist, Joshua Wendland, Ilya Shpitser, Carsten Marr

ICML 2025poster

Feature importance metrics are critical for interpreting machine learning models and understanding the relevance of individual features. However, real-world data often exhibit missingness, thereby complicating how feature importance should be evaluated. We introduce the distinction between two eval…

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