Publications

@InProceedings{pmlr-v206-qing23a,
  title = 	 {\{PF\}$^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization},
  author =       {Qing, Jixiang and Moss, Henry B. and Dhaene, Tom and Couckuyt, Ivo},
  booktitle = 	 {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {2565--2588},
  year = 	 {2023},
  editor = 	 {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
  volume = 	 {206},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {25--27 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v206/qing23a/qing23a.pdf},
  url = 	 {https://proceedings.mlr.press/v206/qing23a.html},
  abstract = 	 {We present Parallel Feasible Pareto Frontier Entropy Search ($\{\mathrm{PF}\}^2$ES) — a novel information-theoretic acquisition function for multi-objective Bayesian optimization supporting unknown constraints and batch queries. Due to the complexity of characterizing the mutual information between candidate evaluations and (feasible) Pareto frontiers, existing approaches must either employ crude approximations that significantly hamper their performance or rely on expensive inference schemes that substantially increase the optimization’s computational overhead. By instead using a variational lower bound, $\{\mathrm{PF}\}^2$ES provides a low-cost and accurate estimate of the mutual information. We benchmark $\{\mathrm{PF}\}^2$ES against other information-theoretic acquisition functions, demonstrating its competitive performance for optimization across synthetic and real-world design problems.}
}
              

@inproceedings{qing2022spectral,
  title={Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty},
  author={Qing, Jixiang and Dhaene, Tom and Couckuyt, Ivo},
  booktitle={International Conference on Machine Learning},
  pages={18096--18121},
  year={2022},
  organization={PMLR}
}
              

@article{qing2022robust,
  title={A robust multi-objective Bayesian optimization framework considering input uncertainty},
  author={Qing, Jixiang and Couckuyt, Ivo and Dhaene, Tom},
  journal={Journal of Global Optimization},
  pages={1--19},
  year={2022},
  publisher={Springer}
}
              

@article{qing2021adaptive,
  title={Adaptive sampling with automatic stopping for feasible region identification in engineering design},
  author={Qing, Jixiang and Knudde, Nicolas and Garbuglia, Federico and Spina, Domenico and Couckuyt, Ivo and Dhaene, Tom},
  journal={Engineering with Computers},
  pages={1--18},
  year={2021},
  publisher={Springer}
}
              

@inproceedings{qing2020bayesian,
  title={Bayesian active learning for electromagnetic structure design},
  author={Qing, Jixiang and Knudde, Nicolas and Couckuyt, Ivo and Spina, Domenico and Dhaene, Tom},
  booktitle={2020 14th European Conference on Antennas and Propagation (EuCAP)},
  pages={1--5},
  year={2020},
  organization={IEEE}
}
              

@inproceedings{qing2019kriging,
  title={Kriging assisted integrated rotor-duct optimization for ducted fan in Hover},
  author={Qing, Jixiang and Hu, Yu and Wang, Yanling and Liu, Zhonghuan and Fu, Xuyang and Liu, Wenmeng},
  booktitle={AIAA Scitech 2019 Forum},
  pages={0007},
  year={2019}
}