20 citations · 60 across the 18 of their papers we have counts for
11 papers · 1 filter
Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Prediction
Ya Song, Laurens Bliek, Yaoxin Wu +1
Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which can model both temporal and spa…
End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW
Abdo Abouelrous, Laurens Bliek, Yaoxin Wu +1
In this work, we consider learning-based applications in routing to solve a Vehicle Routing variant characterized by stochasticity and multiple objectives. Such problems are repres…
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application
Abdo Abouelrous, Laurens Bliek, Adriana F. Gabor +2
Column Generation (CG) is a popular method dedicated to enhancing computational efficiency in large scale Combinatorial Optimization (CO) problems. It reduces the number of decisio…
Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing
Abdo Abouelrous, Laurens Bliek, Adriana F. Gabor +2
In this paper, we address the problem of Column Generation (CG) using Reinforcement Learning (RL). Specifically, we use a RL model based on the attention-mechanism architecture to…
Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networks
Ya Song, Laurens Bliek, Yingqian Zhang
Algorithm selection is a well-known problem where researchers investigate how to construct useful features representing the problem instances and then apply feature-based machine l…
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
Stefano Teso, Laurens Bliek, Andrea Borghesi +4
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variab…
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
Laurens Bliek, Arthur Guijt, Rickard Karlsson +2
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box optimisation problems with expensive objectives, such as hyperparameter tuning or simulatio…
Black-box Mixed-Variable Optimisation using a Surrogate Model that Satisfies Integer Constraints
Laurens Bliek, Arthur Guijt, Sicco Verwer +1
A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evalua…
Black-box Combinatorial Optimization using Models with Integer-valued Minima
Laurens Bliek, Sicco Verwer, Mathijs de Weerdt
When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Sp…
Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions
Laurens Bliek, Hans R. G. W. Verstraete, Michel Verhaegen +1
This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements. The algorithm maintains a surrogat…
Exploration and Exploitation in Visuomotor Prediction of Autonomous Agents
Laurens Bliek
This paper discusses various techniques to let an agent learn how to predict the effects of its own actions on its sensor data autonomously, and their usefulness to apply them to v…