Paul Kobialka — accepted AI-conference papers

2026

Attribution-based Explanations for Markov Decision Processes

Paul Kobialka, Andrea Pferscher, Francesco Leofante, Erika Ábrahám, Silvia Lizeth Tapia Tarifa, Einar Broch Johnsen

IJCAI 2026

Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on attributing importance to static input features at a single point in time, and thus fail to generalize to sequential decision-making settings. This

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2025

Counterfactual Strategies for Markov Decision Processes

Paul Kobialka, Lina Gerlach, Francesco Leofante, Erika Ábrahám, Silvia Lizeth Tapia Tarifa, Einar Broch Johnsen

IJCAI 2025

Counterfactuals are widely used in AI to explain how minimal changes to a model’s input can lead to a different output. However, established methods for computing counterfactuals typically focus on one-step decision-making, and are not directly applicable to sequential decision-making tasks. This pa

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