A Latent Customer Flow Model for Interpretable Predictions of Check-In Counts. - Daniel Tschernutter; Stefan Feuerriegel (IEEE BigData, 2021)

Interpretable Off-Policy Learning via Hyperbox Search. - Daniel Tschernutter; Tobias Hatt; Stefan Feuerriegel (ICML, 2022)

Generalizing off-policy learning under sample selection bias. - Tobias Hatt; Daniel Tschernutter; Stefan Feuerriegel (UAI, 2022)

Advances in Data-Driven Decision-Making: A Mathematical Optimization Perspective. - Daniel Tschernutter (2022)

Interpretable generalized additive neural networks. - Mathias Kraus; Daniel Tschernutter; Sven Weinzierl; Patrick Zschech (Eur. J. Oper. Res., 2024)

A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions. - Daniel Tschernutter; Mathias Kraus; Stefan Feuerriegel (Trans. Mach. Learn. Res., 2024)

Data-driven dynamic police patrolling: An efficient Monte Carlo tree search. - Daniel Tschernutter; Stefan Feuerriegel (Eur. J. Oper. Res., 2025)