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Found 8 papers, 2 papers with code
Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules
Uncertainty representation and quantification are paramount in machine learning and constitute an important prerequisite for safety-critical applications.
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
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.
Second-Order Uncertainty Quantification: Variance-Based Measures
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.
Second-Order Uncertainty Quantification: A Distance-Based Approach
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.
A Novel Bayes' Theorem for Upper Probabilities
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.
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?
Adequate uncertainty representation and quantification have become imperative in various scientific disciplines, especially in machine learning and artificial intelligence.
Conformal Prediction with Partially Labeled Data
While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise.
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?
The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, has recently become quite common in machine learning.
