1 citations · 3 across the 10 of their papers we have counts for
10 papers
Amortized Interventional Forecasting for Multivariate CIR Processes
Andreas Sauter, Sumit Sourabh, Drona Kandhai +1
Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from short rates to credit defaul…
ACTIVA: Amortized Causal Effect Estimation via Variational Autoencoders
Andreas Sauter, Saber Salehkaleybar, Frank van Harmelen +2
Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challenging due to causal ambiguity, rest…
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…
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…
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…
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…
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…
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…
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…
Explainable Fraud Detection with Deep Symbolic Classification
Samantha Visbeek, Erman Acar, Floris den Hengst
There is a growing demand for explainable, transparent, and data-driven models within the domain of fraud detection. Decisions made by fraud detection models need to be explainable…