Zobrazeno 1 - 10
of 165
pro vyhledávání: '"Birbil, Ş. İlker"'
The early detection of Alzheimer's disease (AD) requires the understanding of the relations between a wide range of disease-related features. Analyses that estimate these relations and evaluate their uncertainty are still rare. We address this gap by
Externí odkaz:
http://arxiv.org/abs/2411.07745
The concept of counterfactual explanations (CE) has emerged as one of the important concepts to understand the inner workings of complex AI systems. In this paper, we translate the idea of CEs to linear optimization and propose, motivate, and analyze
Externí odkaz:
http://arxiv.org/abs/2405.15431
When there is a correlation between any pair of targets, one needs a prediction method that can handle vector-valued output. In this setting, multi-target learning is particularly important as it is widely used in various applications. This paper int
Externí odkaz:
http://arxiv.org/abs/2405.15314
Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is known as structure learning. Bayesian methods can me
Externí odkaz:
http://arxiv.org/abs/2307.02603
Autor:
Cinà, Giovanni, Fernandez-Llaneza, Daniel, Deponte, Ludovico, Mishra, Nishant, Röber, Tabea E., Pezzelle, Sandro, Calixto, Iacer, Goedhart, Rob, Birbil, Ş. İlker
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models. Despite their success, some scholars have argued that such methods suffer from a serious flaw: they do not allow a reliable interpretatio
Externí odkaz:
http://arxiv.org/abs/2307.00897
Bayesian methods for learning Gaussian graphical models offer a comprehensive framework that addresses model uncertainty and incorporates prior knowledge. Despite their theoretical strengths, the applicability of Bayesian methods is often constrained
Externí odkaz:
http://arxiv.org/abs/2307.00127
We propose a novel Bayesian inference framework for distributed differentially private linear regression. We consider a distributed setting where multiple parties hold parts of the data and share certain summary statistics of their portions in privac
Externí odkaz:
http://arxiv.org/abs/2301.13778
Autor:
Maragno, Donato, Kurtz, Jannis, Röber, Tabea E., Goedhart, Rob, Birbil, Ş. Ilker, Hertog, Dick den
Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a minimal perturbed data point for which the decision of the model change
Externí odkaz:
http://arxiv.org/abs/2301.11113
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI (XAI) and its promise to render AI devices more transparent a
Externí odkaz:
http://arxiv.org/abs/2301.02080
Two-stage robust optimization problems constitute one of the hardest optimization problem classes. One of the solution approaches to this class of problems is K-adaptability. This approach simultaneously seeks the best partitioning of the uncertainty
Externí odkaz:
http://arxiv.org/abs/2210.11152