Maximilian Weininger — 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

Solving Robust Markov Decision Processes: Generic, Reliable, Efficient

Tobias Meggendorfer, Maximilian Weininger, Patrick Wienhöft

AAAI 2025technical

Markov decision processes (MDP) are a well-established model for sequential decision-making in the presence of probabilities. In *robust* MDP (RMDP), every action is associated with an *uncertainty set* of probability distributions, modelling that transition probabilities are not known precisely. Ba…

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