30 citations · 91 across the 12 of their papers we have counts for
8 papers · 1 filter
Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures
Yuko Kato, David M. J. Tax, Marco Loog
Conformal prediction, which makes no distributional assumptions about the data, has emerged as a powerful and reliable approach to uncertainty quantification in practical applicati…
Learning From Scenarios for Stochastic Repairable Scheduling
Kim van den Houten, David M. J. Tax, Esteban Freydell +1
When optimizing problems with uncertain parameter values in a linear objective, decision-focused learning enables end-to-end learning of these values. We are interested in a stocha…
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification
Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax
Photoplethysmography (PPG) signals, typically acquired from wearable devices, hold significant potential for continuous fitness-health monitoring. In particular, heart conditions t…
iPINNs: Incremental learning for Physics-informed neural networks
Aleksandr Dekhovich, Marcel H. F. Sluiter, David M. J. Tax +1
Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs). However, finding a set of neural network parameters…
A view on model misspecification in uncertainty quantification
Yuko Kato, David M. J. Tax, Marco Loog
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence…
Continual Prune-and-Select: Class-incremental learning with specialized subnetworks
Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter +1
The human brain is capable of learning tasks sequentially mostly without forgetting. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning one task…