Lu, Congwen, and Paulson, Joel A. No-regret constrained Bayesian optimization of noisy and expensive hybrid models using differentiable quantile function approximations. Retrieved from https://par.nsf.gov/biblio/10482895. Journal of Process Control 131.C Web. doi:10.1016/j.jprocont.2023.103085.

Lu, Congwen, & Paulson, Joel A. No-regret constrained Bayesian optimization of noisy and expensive hybrid models using differentiable quantile function approximations. Journal of Process Control, 131 (C). Retrieved from https://par.nsf.gov/biblio/10482895. https://doi.org/10.1016/j.jprocont.2023.103085

Lu, Congwen, and Paulson, Joel A. "No-regret constrained Bayesian optimization of noisy and expensive hybrid models using differentiable quantile function approximations". Journal of Process Control 131 (C). Country unknown/Code not available: Elsevier. https://doi.org/10.1016/j.jprocont.2023.103085. https://par.nsf.gov/biblio/10482895.

@article{osti_10482895,
place = {Country unknown/Code not available}, title = {No-regret constrained Bayesian optimization of noisy and expensive hybrid models using differentiable quantile function approximations}, url = {https://par.nsf.gov/biblio/10482895}, DOI = {10.1016/j.jprocont.2023.103085}, abstractNote = {}, journal = {Journal of Process Control}, volume = {131}, number = {C}, publisher = {Elsevier}, author = {Lu, Congwen and Paulson, Joel A.}, }