123 citations · 284 across the 59 of their papers we have counts for
9 papers · 1 filter
Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis
Thalea Schlender, Peter A. N. Bosman, Tanja Alderliesten
Survival analysis concerns the task of predicting the time until an event occurs. Often used in the medical field, survival analysis deals with incomplete (i.e., censored) data, fo…
Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
Damy M. F. Ha, Thalea Schlender, Yvette M. van der Linden +2
Bayesian Networks (BNs) are of interest from an explainable AI viewpoint, offering transparent probabilistic models for decision support. Baymex is a recently introduced multi-obje…
Sharing Knowledge without Sharing Data: Stitches can improve ensembles of disjointly trained models
Arthur Guijt, Dirk Thierens, Ellen Kerkhof +3
Deep learning has been shown to be very capable at performing many real-world tasks. However, this performance is often dependent on the presence of large and varied datasets. In s…
Iterated Population Based Training with Task-Agnostic Restarts
Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman
Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks. HPO algorithms from the Population Based Training (PBT) family are efficie…
Learning Discretized Bayesian Networks with GOMEA
Damy M. F. Ha, Tanja Alderliesten, Peter A. N. Bosman
Bayesian networks model relationships between random variables under uncertainty and can be used to predict the likelihood of events and outcomes while incorporating observed evide…
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search
Alexander Chebykin, Arkadiy Dushatskiy, Tanja Alderliesten +1
In this work, we show that simultaneously training and mixing neural networks is a promising way to conduct Neural Architecture Search (NAS). For hyperparameter optimization, reusi…
Multi-Objective Population Based Training
Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten +1
Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems…
Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization
Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers +2
Real-world problems are often multi-objective with decision-makers unable to specify a priori which trade-off between the conflicting objectives is preferable. Intuitively, buildin…
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms
Marco Virgolin, Ziyuan Wang, Tanja Alderliesten +1
Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatm…