no code implementations • 18 Apr 2024 • Paul Hofman, Yusuf Sale, Eyke Hüllermeier
Uncertainty representation and quantification are paramount in machine learning and constitute an important prerequisite for safety-critical applications.
no code implementations • 7 Mar 2024 • Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio
We address this issue by proposing ShapleyBO, a framework for interpreting BO's proposals by game-theoretic Shapley values. They quantify each parameter's contribution to BO's acquisition function.
no code implementations • 30 Dec 2023 • Yusuf Sale, Paul Hofman, Lisa Wimmer, Eyke Hüllermeier, Thomas Nagler
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process in real-world applications.
no code implementations • 2 Dec 2023 • Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier
In the past couple of years, various approaches to representing and quantifying different types of predictive uncertainty in machine learning, notably in the setting of classification, have been proposed on the basis of second-order probability distributions, i. e., predictions in the form of distributions on probability distributions.
no code implementations • 13 Jul 2023 • Michele Caprio, Yusuf Sale, Eyke Hüllermeier, Insup Lee
In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set $A$, when the prior lies in a class of probability measures $\mathcal{P}$ and the likelihood is precise.
no code implementations • 16 Jun 2023 • Yusuf Sale, Michele Caprio, Eyke Hüllermeier
Adequate uncertainty representation and quantification have become imperative in various scientific disciplines, especially in machine learning and artificial intelligence.
1 code implementation • 1 Jun 2023 • Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier
While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise.
1 code implementation • 7 Sep 2022 • Lisa Wimmer, Yusuf Sale, Paul Hofman, Bern Bischl, Eyke Hüllermeier
The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, has recently become quite common in machine learning.