1 citations · 2 across the 11 of their papers we have counts for
12 papers
Evaluating and Recalibrating Probabilistic Classifiers from One-Bit Threshold Queries
Roman Plaud, Antoine Saillenfest, Matthieu Labeau +2
Standard calibration evaluation and recalibration methods assume access to predicted probabilities. We study one-bit threshold access, a setting in which probabilities are hidden a…
Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier +1
Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks…
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra
Mira Jürgens, Gaetan De Waele, Morteza Rakhshaninejad +1
Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still exhibit significant error rates.…
Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction
Morteza Rakhshaninejad, Mira Jurgens, Nicolas Dewolf +1
Accurate drug-target interaction (DTI) prediction with machine learning models is essential for drug discovery. Such models should also provide a credible representation of their u…
Position: Epistemic uncertainty estimation methods are fundamentally incomplete
Sebastián Jiménez, Mira Jürgens, Willem Waegeman
Identifying and disentangling sources of predictive uncertainty is essential for trustworthy supervised learning. We argue that widely used second-order methods that disentangle al…
Optimal Conformal Prediction under Epistemic Uncertainty
Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies +3
Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees. In practi…
A calibration test for evaluating set-based epistemic uncertainty representations
Mira Jürgens, Thomas Mortier, Eyke Hüllermeier +2
The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilist…
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
Thomas Mortier, Alireza Javanmardi, Yusuf Sale +2
Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conform…
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition
Arthur Hoarau, Benjamin Quost, Sébastien Destercke +1
To generate accurate and reliable predictions, modern AI systems need to combine data from multiple modalities, such as text, images, audio, spreadsheets, and time series. Multi-mo…
Assessment of Uncertainty Quantification in Universal Differential Equations
Nina Schmid, David Fernandes del Pozo, Willem Waegeman +1
Scientific Machine Learning is a new class of approaches that integrate physical knowledge and mechanistic models with data-driven techniques for uncovering governing equations of…
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
Mira Jürgens, Nis Meinert, Viktor Bengs +2
Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both ale…