137 citations · 488 across the 26 of their papers we have counts for
26 papers
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…
Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags
Kim van den Houten, Léon Planken, Esteban Freydell +2
This study investigates scheduling strategies for the stochastic resource-constrained project scheduling problem with maximal time lags (SRCPSP/max)). Recent advances in Constraint…
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…
Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability
Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax
With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding…