Search Results for author:
Found 9 papers, 2 papers with code
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO
Multi-objective optimization is a widely studied problem in diverse fields, such as engineering and finance, that seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives.
$\{\text{PF}\}^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization
We present Parallel Feasible Pareto Frontier Entropy Search ($\{\text{PF}\}^2$ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization supporting unknown constraints and batch query.
Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data
To improve the predictive performance and reliability of the models, early and late sensor fusion methods are explored in this work.
Generating an Explainable ECG Beat Space With Variational Auto-Encoders
Electrocardiogram signals are omnipresent in medicine.
GPflowOpt: A Bayesian Optimization Library using TensorFlow
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced.
Positive blood culture detection in time series data using a BiLSTM network
The presence of bacteria or fungi in the bloodstream of patients is abnormal and can lead to life-threatening conditions.
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes
Student-$t$ processes have recently been proposed as an appealing alternative non-parameteric function prior.
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty
When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy.
Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations
A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived.
