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20232026
most citedOn the Potential of Network-Based Features for Fraud Detection

1 citations · 3 across the 10 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.MA2024

Learning in Multi-Objective Public Goods Games with Non-Linear Utilities

Nicole Orzan, Erman Acar, Davide Grossi +2

Addressing the question of how to achieve optimal decision-making under risk and uncertainty is crucial for enhancing the capabilities of artificial agents that collaborate with or…

cs.AI2024

CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research

Andreas W M Sauter, Erman Acar, Aske Plaat

Research on causal effects often relies on synthetic data due to the scarcity of real-world datasets with ground-truth effects. Since current data-generating tools do not always me…

cs.AI2024★ 1 cited

CAGE: Causality-Aware Shapley Value for Global Explanations

Nils Ole Breuer, Andreas Sauter, Majid Mohammadi +1

As Artificial Intelligence (AI) is having more influence on our everyday lives, it becomes important that AI-based decisions are transparent and explainable. As a consequence, the…

cs.CV2024

PFStorer: Personalized Face Restoration and Super-Resolution

Tuomas Varanka, Tapani Toivonen, Soumya Tripathy +2

Recent developments in face restoration have achieved remarkable results in producing high-quality and lifelike outputs. The stunning results however often fail to be faithful with…

q-fin.RM2024★ 1 cited

On the Potential of Network-Based Features for Fraud Detection

Catayoun Azarm, Erman Acar, Mickey van Zeelt

Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses. Conventional rule-based systems struggle to keep pace wi…

cs.LG2024

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning

Andreas W. M. Sauter, Nicolò Botteghi, Erman Acar +1

Causal discovery is the challenging task of inferring causal structure from data. Motivated by Pearl's Causal Hierarchy (PCH), which tells us that passive observations alone are no…

cs.MA2024★ 1 cited

Emergent Cooperation under Uncertain Incentive Alignment

Nicole Orzan, Erman Acar, Davide Grossi +1

Understanding the emergence of cooperation in systems of computational agents is crucial for the development of effective cooperative AI. Interaction among individuals in real-worl…