1 citations · 2 across the 8 of their papers we have counts for
8 papers
XAI-Arena: Can LLMs Assess the Quality of XAI Explanations?
Yanfei Hu Fleischhauer, Alona Zharova, Nadja Klein +1
Evaluating the quality of explanations produced by explainable AI (XAI) methods remains challenging because existing approaches often rely on subjective human judgment, limiting re…
Copula-based models for spatially dependent cylindrical data
Francesca Labanca, Anna Gottard, Nadja Klein
Cylindrical data frequently arise across various scientific disciplines, including meteorology (e.g., wind direction and speed), oceanography (e.g., marine current direction and sp…
Bayesian Additive Regression Tree Copula Processes for Scalable Distributional Prediction
Jan Martin Wenkel, Michael Stanley Smith, Nadja Klein
We show how to construct the implied copula process of response values from a Bayesian additive regression tree (BART) model with prior on the leaf node variances. This copula proc…
Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation
Tobias Fuchs, Nadja Klein
Real-world data is frequently noisy and ambiguous. In crowdsourcing, for example, human annotators may assign conflicting class labels to the same instances. Partial-label learning…
Beware of "Explanations" of AI
David Martens, Galit Shmueli, Theodoros Evgeniou +14
Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artifici…
Density regression via Dirichlet process mixtures of normal structured additive regression models
María Xosé Rodríguez-Álvarez, Vanda Inácio, Nadja Klein
Within Bayesian nonparametrics, dependent Dirichlet process mixture models provide a highly flexible approach for conducting inference about the conditional density function. Howev…
Bayesian Effect Selection in Additive Models with an Application to Time-to-Event Data
Paul Bach, Nadja Klein
Accurately selecting and estimating smooth functional effects in additive models with potentially many functions is a challenging task. We introduce a novel Demmler-Reinsch basis e…
Scalable Estimation for Structured Additive Distributional Regression Through Variational Inference
Jana Kleinemeier, Nadja Klein
Structured additive distributional regression models offer a versatile framework for estimating complete conditional distributions by relating all parameters of a parametric distri…