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Found 9 papers, 3 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.
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow.
$\{\text{PF}\}^2$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.
Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding
Adhesive joints are increasingly used in industry for a wide variety of applications because of their favorable characteristics such as high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance, and fatigue resistance.
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems
Another concern that is often cited when designing smart home applications is the resilience of these applications against cyberattacks.
GPflowOpt: A Bayesian Optimization Library using TensorFlow
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced.
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.
