8 citations · 27 across the 19 of their papers we have counts for
19 papers
Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
Uncertainty representation and quantification are paramount in machine learning and constitute an important prerequisite for safety-critical applications. In this paper, we propose…
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
Julian Rodemann, Federico Croppi, Philipp Arens +7
Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often consider…
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification
Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli +2
Addressing the limitations of individual attribution scores via the Shapley value (SV), the field of explainable AI (XAI) has recently explored intricate interactions of features o…
Conformalized Credal Set Predictors
Alireza Javanmardi, David Stutz, Eyke Hüllermeier
Credal sets are sets of probability distributions that are considered as candidates for an imprecisely known ground-truth distribution. In machine learning, they have recently attr…
Second-Order Uncertainty Quantification: Variance-Based Measures
Yusuf Sale, Paul Hofman, Lisa Wimmer +2
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process…
Identifying Copeland Winners in Dueling Bandits with Indifferences
Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
We consider the task of identifying the Copeland winner(s) in a dueling bandits problem with ternary feedback. This is an underexplored but practically relevant variant of the conv…