iamrp.net-t0Jx-1793296010-t093400644 1793296010 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 2023 2 2 Ali Hassan Noor Siddiqui Bilal Khan Sana Malik 2023 10.68159/t093400644 https://iamrp.net/pub/journal/1/article/t093400644 Dockès J, Varoquaux G, Poline JB. Preventing dataset shift from breaking machine-learning biomarkers. Gigascience. 2021;10(9):giab055. Wang Y, Wald I, Wu Q, Usher W, Johnson CR. IEEE transactions on visualization and computer graphics. IEEE Trans Vis Comput Graph. 2019;25(6):2168-80. Guo LL, Pfohl SR, Fries J, Posada J, Fleming SL, Aftandilian C, et al. Systematic review of approaches to preserve machine learning performance in the presence of temporal dataset shift in clinical medicine. Appl Clin Inform. 2021;12(4):808-15. 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. Gubernatis JE, Lookman TJ. Machine learning in materials design and discovery: Examples from the present and suggestions for the future. Phys Rev Mater. 2018;2(12):120301. Farahani A, Voghoei S, Rasheed K, Arabnia HR. A brief review of domain adaptation. In: Adv Data Sci Info Eng. Cham: Springer; 2021. p. 877-94. Liu J, Shen Z, He Y, Zhang X, Xu R, Yu H, et al. Towards out-of-distribution generalization: A survey. arXiv [Preprint]. 2021:arXiv:2108.13624. Wei S, Guo Y, Wen X, Li S, Zhu L, Zhu R. Admeood: Out-of-distribution benchmark for drug property prediction. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). New York, NY: IEEE; 2023. p. 387-94. Abbasian Dehkordi S, Farajzadeh K, Rezazadeh J, Farahbakhsh R, Sandrasegaran K, Abbasian Dehkordi M. A survey on data aggregation techniques in IoT sensor networks. Wirel Netw. 2020;26(2):1243-63. Cui P, Wang J. Out-of-distribution (OOD) detection based on deep learning: A review. Electronics. 2022;11(21):3500. Ranasinghe C, Degbelo A. Encoding variables, evaluation criteria, and evaluation methods for data physicalisations: A review. Multimodal Technol Interact. 2023;7(7):73. Koh PW, Sagawa S, Marklund H, Xie SM, Zhang M, Balsubramani A, et al. Wilds: A benchmark of in-the-wild distribution shifts. In: International Conference on Machine Learning. Brookline, MA: PMLR; 2021. p. 5637-64. He H, Queen O, Koker T, Cuevas C, Tsiligkaridis T, Zitnik M. Domain adaptation for time series under feature and label shifts. In: International Conference on Machine Learning. Brookline, MA: PMLR; 2023. p. 12746-74. Kong L, Xie S, Yao W, Zheng Y, Chen G, Stojanov P, et al. Partial disentanglement for domain adaptation. In: International Conference on Machine Learning. Brookline, MA: PMLR; 2022. p. 11455-72. Guan H, Liu M. DomainATM: Domain adaptation toolbox for medical data analysis. Neuroimage. 2023;268:119863. Laparra E, Bethard S, Miller TA. Rethinking domain adaptation for machine learning over clinical language. JAMIA Open. 2020;3(2):146-50. Goel P, Ganatra A. Unsupervised domain adaptation for image classification and object detection using guided transfer learning approach and JS divergence. Sensors. 2023;23(9):4436. Panda R, Kalmady SV, Greiner R. Multi-source domain adaptation techniques for mitigating batch effects: A comparative study. Front Neuroinform. 2022;16:805117. Kubik C, Molitor DA, Rojahn M, Groche P. Deep learning-based domain adaptation for a generalized detection of wear phenomena during blanking. Manuf Lett. 2023;35:904-14. Rabanser S, Günnemann S, Lipton Z. Failing loudly: An empirical study of methods for detecting dataset shift. Adv Neural Inf Process Syst. 2019;32. Chen L, Zaharia M, Zou JY. Estimating and explaining model performance when both covariates and labels shift. Adv Neural Inf Process Syst. 2022;35:11467-79. Chang J, Uehara M, Sreenivas D, Kidambi R, Sun W. Mitigating covariate shift in imitation learning via offline data with partial coverage. Adv Neural Inf Process Syst. 2021;34:965-79. Shao JJ, Xu Y, Cheng Z, Li YF. Active model adaptation under unknown shift. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY: ACM; 2022. p. 1558-66. Qiu H, Dobriban E, Tchetgen Tchetgen E. Prediction sets adaptive to unknown covariate shift. J R Stat Soc Ser B Stat Methodol. 2023;85(5):1680-705. Germano D, Sciaraffa N, Ronca V, Giorgi A, Trulli G, Borghini G, et al. Unsupervised detection of covariate shift due to changes in EEG headset position: Towards an effective out-of-lab use of passive brain–computer interface. Appl Sci. 2023;13(23):12800. Chiang CW, Yin M. You’d better stop! Understanding human reliance on machine learning models under covariate shift. In: Proceedings of the 13th ACM Web Science Conference. New York, NY: ACM; 2021. p. 120-9. Xu R, Baracaldo N, Joshi J. Privacy-preserving machine learning: Methods, challenges and directions. arXiv [Preprint]. 2021:arXiv:2108.04417. Nyborg J, Pelletier C, Lefèvre S, Assent I. TimeMatch: Unsupervised cross-region adaptation by temporal shift estimation. ISPRS J Photogramm Remote Sens. 2022;188:301-13.