Note from the Editor
Published Online:13 Feb 2026https://doi.org/10.1287/ijoc.2026.ed.v38.n1
References
- (2021) Bayesian optimization of function networks. Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J, eds. Adv. Neural Inform. Processing Systems 34 (NeurIPS 2021) (Curran Associates, Inc., Red Hook, NY), 14463–14475.Google Scholar
- (2020) BoTorch: A framework for efficient Monte-Carlo Bayesian optimization. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. Adv. Neural Inform. Processing Systems 33 (NeurIPS 2020) (Curran Associates, Inc., Red Hook, NY), 21524–21538.Google Scholar
- (2024) Bayesian optimization of function networks with partial evaluations. Salakhutdinov R, Kolter Z, Heller K, Weller A, Oliver N, Scarlett J, Berkenkamp F, eds. Proc. 41st Internat. Conf. Machine Learn. (PMLR, New York), 4752–4784.Google Scholar
- (2013) Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing. Dasgupta S, McAllester D, eds. Proc. 30th Internat. Conf. Machine Learn. (PMLR, New York), 64–72.Google Scholar
- (2008) A knowledge-gradient policy for sequential information collection. SIAM J. Control Optim. 47(5):2410–2439.Crossref, Google Scholar
- (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS J. Comput. 21(4):599–613.Link, Google Scholar
- (2023) Bayesian Optimization (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2010) Towards Gaussian process-based optimization with finite time horizon. Giovagnoli A, Atkinson A, Torsney B, May C, eds. mODa 9 Adv. Model-Oriented Design Anal. (Springer-Verlag, Berlin), 89–96.Google Scholar
- (1996) Bayesian look ahead one-stage sampling allocations for selection of the best population. J. Statist. Planning Inference 54(2):229–244.Crossref, Google Scholar
- (2018) Optimal learning for stochastic optimization with nonlinear parametric belief models. SIAM J. Optim. 28(3):2327–2359.Crossref, Google Scholar
- (2020) Optimal learning with local nonlinear parametric models over continuous designs. SIAM J. Sci. Comput. 42(4):A2134–A2157.Crossref, Google Scholar
- (2022) Preference exploration for efficient Bayesian optimization with multiple outcomes. Camps-Valls G, Ruiz FJR, Valera I, eds. Proc. 25th Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 4235–4258.Google Scholar
- (2011) Hierarchical knowledge gradient for sequential sampling. J. Machine Learn. Res. 12(90):2931–2974.Google Scholar
- (1972) Bayesian methods of search for an extremum. Avtomatika i Vychislitel’naya Tekhnika 6(3):53–62.Google Scholar
- (2011) The knowledge-gradient algorithm for sequencing experiments in drug discovery. INFORMS J. Comput. 23(3):346–363.Link, Google Scholar
- (2018) Continuous multi-task Bayesian optimisation with correlation. Eur. J. Oper. Res. 270(3):1074–1085.Crossref, Google Scholar
- (2022) Bayesian optimization allowing for common random numbers. Oper. Res. 70(6):3457–3472.Link, Google Scholar
- (2017) Multi-information source optimization. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems 30 (NIPS 2017) (Curran Associates, Inc., Red Hook, NY), 4291–4301.Google Scholar
- (2012) Optimal Learning (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
- (2012) The knowledge gradient algorithm for a general class of online learning problems. Oper. Res. 60(1):180–195.Link, Google Scholar
- (2012) Practical Bayesian optimization of machine learning algorithms. Pereira F, Burges CJ, Bottou L, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems 25 (NIPS 2012) (Curran Associates, Inc., Red Hook, NY), 2951–2959.Google Scholar
- (2017) Bayesian optimization with gradients. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems 30 (NIPS 2017) (Curran Associates, Inc., Red Hook, NY), 5273–5284.Google Scholar

