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Vrije Universiteit Brussel

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Selected work

Representative Papers

Optimal payoff under Bregman-Wasserstein divergence constraints

Nov 27, 2024

This paper studies the optimal payoff selection problem for expected utility maximizers under a Bregman–Wasserstein (BW) divergence constraint, designed to control deviation from a reference payoff while allowing asymmetric penalties for upside and downside deviations—better aligning with real-world investment objectives. Methodologically, it provides the first analytical solution to the optimal payoff structure under BW divergence constraints, employing a convex function φ to flexibly encode directional deviation preferences and thereby overcoming the symmetry limitation inherent in classical Wasserstein distance. By integrating convex analysis, optimal transport theory, and stochastic optimization, the authors formulate a utility maximization framework regularized by a Bregman penalty term. Theoretically, they derive a closed-form expression for the optimal payoff. Numerical experiments demonstrate that tuning φ enables precise calibration of risk attitudes and significantly improves alignment between payoff allocation and investor-specific goals.

2 citationsRead paper

Fixpoint Semantics for DatalogMTL with Negation

Jan 07, 2026Electronic Proceedings in Theoretical Computer Science

This work addresses the lack of a unified and rigorous semantics for DatalogMTL with negation by systematically introducing Approximation Fixpoint Theory (AFT) into the language for the first time. By integrating metric temporal logic operators with non-monotonic reasoning techniques, the paper provides concise definitions of four key semantics: stable models, well-founded models, Kripke-Kleene models, and supported models. The proposed framework establishes a formally coherent and highly expressive semantic foundation. Moreover, it demonstrates that the derived stable model semantics is equivalent to the existing definition based on here-and-there temporal logic, thereby validating the effectiveness and applicability of AFT in the context of temporal logic programming.

1 citationsRead paper

GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method

Sep 17, 2024arXiv.org

To address the lack of interpretability in temporal graph regression models, this paper proposes the first interpretable and traceable temporal graph neural network framework by integrating the Information Bottleneck (IB) principle with prototype learning. Methodologically: (1) it derives a novel mutual information bound tailored to graph-structured data, enabling joint optimization of feature compression and discriminability; (2) it introduces an unsupervised auxiliary classification head to enhance prototype concept disentanglement and improve semantic interpretability of the bottleneck layer; and (3) it unifies multi-task learning with prototype-guided training for temporal GNNs. Experiments on real-world traffic datasets demonstrate that the method significantly outperforms existing baselines in both prediction accuracy and interpretability metrics—including prototype relevance and attribution consistency—achieving a principled balance between high predictive performance and model transparency.

1 citationsRead paper
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