iamrp.net-Sxvy-1791561620-b837828985 1791561620 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Artificial Intelligence for Materials Science J. Artif. Intell. Mater. Sci. 3149-8957 2024 3 1 Sven Larsson Erik Johansson Anna Nilsson 2024 10.68159/b837828985 https://iamrp.net/pub/journal/1/article/b837828985 Kochenderfer MJ, Wheeler TA, Wray KH. Algorithms for decision making. Cambridge (MA): MIT Press; 2022. Wang J, Ma X, Xu Z, Zhan J. Regret theory-based three-way decision model in hesitant fuzzy environments and its application to medical decision. IEEE Trans Fuzzy Syst. 2022;30(12):5361-75. Zhang N, Zheng S, Tian L, Wei G. Study the supplier evaluation and selection in supply chain disruption risk based on regret theory and VIKOR method. Kybernetes. 2024;53(10):3848-74. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. Schmidt J, Marques MR, Botti S, Marques MA. Recent advances and applications of machine learning in solid-state materials science. NPJ Comput Mater. 2019;5(1):83. Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2(4):0121. Montoya JH, Aykol M, Anapolsky A, Gopal CB, Herring PK, Hummelshøj JS, et al. Toward autonomous materials research: Recent progress and future challenges. Appl Phys Rev. 2022;9(1). Nguyen V, Gupta S, Rana S, Li C, Venkatesh S. Regret for expected improvement over the best-observed value and stopping condition. In: Asian Conference on Machine Learning. Seoul: PMLR; 2017. p. 279-94. Liang Q, Gongora AE, Ren Z, Tiihonen A, Liu Z, Sun S, et al. Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains. NPJ Comput Mater. 2021;7(1):188. Iwazaki S, Takeno S, Tanabe T, Irie M. Failure-aware Gaussian process optimization with regret bounds. Adv Neural Inf Process Syst. 2023;36:24388-400. Scarlett J. Tight regret bounds for Bayesian optimization in one dimension. In: International Conference on Machine Learning. Stockholm: PMLR; 2018. p. 4500-8. Sim RH, Zhang Y, Low BK, Jaillet P. Collaborative Bayesian optimization with fair regret. In: International Conference on Machine Learning. PMLR; 2021. p. 9691-701. Krishnamurthy SK, Zhan R, Athey S, Brunskill E. Proportional response: contextual bandits for simple and cumulative regret minimization. Adv Neural Inf Process Syst. 2023;36:30255-66. Frazier PI. A tutorial on Bayesian optimization. arXiv [Preprint]. 2018:arXiv:1807.02811. Kusne AG, Yu H, Wu C, Zhang H, Hattrick-Simpers J, DeCost B, et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat Commun. 2020;11(1):5966. Xu R, Yu Y, Zhang C, Ali MK, Ho JC, Yang C. Counterfactual and factual reasoning over hypergraphs for interpretable clinical predictions on EHR. In: Machine Learning for Health. New Orleans, LA: PMLR; 2022. p. 259-78. Hur T, Allenby GM. A choice model of utility maximization and regret minimization. J Mark Res. 2022;59(6):1235-51. Raschka S, Mirjalili V. Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Birmingham: Packt Publishing Ltd; 2019. Deng Z, Zheng Y, Zhang J, Liu P, Zhu Z. Eco-environmental regret-aware optimization of networked multi-energy microgrids with fully carbon elimination and electric vehicles’ promotion. Sustain Cities Soc. 2024;115:105807. Ommer J, Kalas M, Neumann J, Blackburn S, Cloke HL. Turning regret into future disaster preparedness with no-regrets. EGUsphere. 2024;2024:1-5. Azar MG, Osband I, Munos R. Minimax regret bounds for reinforcement learning. In: International Conference on Machine Learning. Sydney: PMLR; 2017. p. 263-72. Wallis CJ, Zhao Z, Huang LC, Penson DF, Koyama T, Kaplan SH, et al. Association of treatment modality, functional outcomes, and baseline characteristics with treatment-related regret among men with localized prostate cancer. JAMA Oncol. 2022;8(1):50-9. Braverman M, Mao J, Schneider J, Weinberg M. Selling to a no-regret buyer. In: Proceedings of the 2018 ACM Conference on Economics and Computation. New Orleans, LA: ACM; 2018. p. 523-38. Wang X, Jin Y, Schmitt S, Olhofer M. Recent advances in Bayesian optimization. ACM Comput Surv. 2023;55(13s):1-36. Farina G, Kroer C, Sandholm T. Regret circuits: composability of regret minimizers. In: International Conference on Machine Learning. Long Beach, CA: PMLR; 2019. p. 1863-72. Mishra AK, Tsionas MG. A minimax regret approach to decision making under uncertainty. J Agric Econ. 2020;71(3):698-718. Chehade M, Mccarthy MM, Squires A. Patient‐related decisional regret: an evolutionary concept analysis. J Clin Nurs. 2024;33(11):4484-503. Asas J, Hawkins M. Does spirituality affect your amount of regret? Available from: https://opus.govst.edu/research_day/2022/sessions/7/. Brown N, Lerer A, Gross S, Sandholm T. Deep counterfactual regret minimization. In: International Conference on Machine Learning. Long Beach, CA: PMLR; 2019. p. 793-802.