Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary
Published Online:23 Mar 2026https://doi.org/10.1287/isre.2022.0537
References
- (2025) The critical challenge of using large-scale digital experiment platforms for scientific discovery. MIS Quart. 49(1):1–28.Crossref, Google Scholar
- (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.Link, Google Scholar
- (2018) Deep learning using rectified linear units (ReLU). Preprint, submitted March 22, https://arxiv.org/abs/1803.08375v1.Google Scholar
- (2018) Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Press, Boston).Google Scholar
- (2023) Zero-day attack detection: A systematic literature review. Artificial Intelligence Rev. 56(10):10733–10811.Crossref, Google Scholar
- (2020) A deep learning architecture for psychometric natural language processing. ACM Trans. Inform. Systems 38(1):1–29.Crossref, Google Scholar
- (2019) Invariant risk minimization. Preprint, submitted July 5, https://arxiv.org/abs/1907.02893.Google Scholar
- (2018) Metareg: Towards domain generalization using meta-regularization. Proc. 32nd Internat. Conf. Neural Inform. Processing Systems, vol. 31 (Curran Associates Inc., Red Hook, NY), 1006–1016.Google Scholar
- (2013) Robust solutions of optimization problems affected by uncertain probabilities. Management Sci. 59(2):341–357.Link, Google Scholar
- (2015) Predictive analytics: Predictive modeling at the micro level. IEEE Intelligent Systems 30(3):6–8.Crossref, Google Scholar
- (2019) Hallucinating agnostic images to generalize across domains. IEEE/CVF Internat. Conf. Comput. Vision Workshop (IEEE, Piscataway, NJ), 3227–3234.Google Scholar
- (2021) SWAD: Domain generalization by seeking flat minima. Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman VJ, eds. Proc. 35th Intern. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 22405–22418.Google Scholar
- (2000) CRISP-DM 1.0: Step-by-step data mining guide. SPSS Inc. 9(13):1–73.Google Scholar
- (2016) XGboost: A scalable tree boosting system. Krishnapuram B, Shah M, Smola AJ, Aggarwal CC, Shen D, Rastogi R, eds. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 785–794.Google Scholar
- (2012) Business intelligence and analytics: From big data to big impact. MIS Quart. 36(4):1165–1188.Crossref, Google Scholar
- (2016) Doctor AI: Predicting clinical events via recurrent neural networks. Finale D, Jim F, David K, Byron W, Jenna W, eds. Machine Learn. Healthcare Conf. vol. 56 (PMLR, New York), 301–318.Google Scholar
- (2021) Statistics of robust optimization: A generalized empirical likelihood approach. Math. Oper. Res. 46(3):946–969.Link, Google Scholar
- (2016) Domain-adversarial training of neural networks. J. Machine Learn. Res. 17(1):2096–2030.Google Scholar
- (2023) Distributionally robust stochastic optimization with Wasserstein distance. Math. Oper. Res. 48(2):603–655.Link, Google Scholar
- (2024) Benchmarking distribution shift in tabular data with tableshift. Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, eds. Proc. 37th Internat. Conf. Neural Inform. Processing Systems, vol. 36 (Curran Associates Inc., Red Hook, NY), 53385–53432.Google Scholar
- (2011) Domain adaptation for large-scale sentiment classification: A deep learning approach. Getoor L, Scheffer T, eds. Proc. 28th Internat. Conf. Machine Learn. (Omnipress, Madison, WI), 513–520.Google Scholar
- (2014) Explaining and harnessing adversarial examples. Preprint, submitted December 20, https://arxiv.org/abs/1412.6572v1.Google Scholar
- (2021) In search of lost domain generalization. Proc. 9th Internat. Conf. Learning Representations (ICLR, Appleton, WI).Google Scholar
- (2023) Predict the future from the past? On the temporal data distribution shift in financial sentiment classifications. Houda B, Juan P, Kalika B, eds. Proc. 2023 Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 1029–1038.Google Scholar
- (2022) Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Sci. Rep. 12(1):2726.Crossref, Google Scholar
- (2024) Domain generalization for enhanced predictions of hospital readmission on unseen domains among patients with diabetes. Artificial Intelligence Medicine 158:103010.Crossref, Google Scholar
- (1997) Long short-term memory. Neural Comput. 9(8):1735–1780.Crossref, Google Scholar
- (2020) Sharing is caring—Data sharing initiatives in healthcare. Internat. J. Environ. Res. Public Health 17(9):3046.Crossref, Google Scholar
- (2020) Diva: Domain invariant variational autoencoders. Arbel T, Ben Ayed I, de Bruijne M, Descoteaux M, Lombaert H, Pal C, eds. Proc. Third Conf. Medical Imaging Deep Learn., vol. 121 (PMLR, Cambridge, MA), 322–348.Google Scholar
- (1932) A History of Psychology in Autobiography, Carl M, ed., vol. II (Clark University Press, Worcester, MA), 207--235.Google Scholar
- (2024) Domain generalization through meta-learning: A survey. Artificial Intelligence Rev. 57(10):285.Crossref, Google Scholar
- (2018) Advanced customer analytics: Strategic value through integration of relationship-oriented big data. J. Management Inform. Systems 35(2):540–574.Crossref, Google Scholar
- (2025) From policy to practice: Research directions for trustworthy and responsible AI “by design.” IEEE Intelligent Systems 40(5):45–51.Crossref, Google Scholar
- (2018) Learning to generalize: Meta-learning for domain generalization. McIlraith SA, Weinberger KQ, eds. Proc. 33nd AAAI Conf. Artificial Intelligence &30thInnovative Applications of Artificial Intelligence Conf. & 8th AAAI Sympos. Educational Adv. Artificial Intelligence (AAAI Press, Palo Alto, CA), 3490--3497.Google Scholar
- (2021) A simple feature augmentation for domain generalization. Proc. IEEE/CVF Internat. Conf. Comput. Vision (IEEE Computer Society, Washington, DC), 8886–8895.Google Scholar
- (2023) Smart natural disaster relief: Assisting victims with artificial intelligence in lending. Inform. Systems Res. 35(2):489–504.Google Scholar
- (2017) A unified approach to interpreting model predictions. Guyon I, Von LU, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Proc. 31st International Conf. Neural Inform. Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY), 4768–4777. Google Scholar
- (2018) Towards deep learning models resistant to adversarial attacks. 6th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).Google Scholar
- (2021) Domain generalization using causal matching. Meila M, Zhang T, eds. 38th Internat. Conf. Machine Learn. (PMLR, New York), 7313–7324.Google Scholar
- (2016) Stochastic gradient methods for distributionally robust optimization with f-divergences. Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett R, eds. Proc. 30th Internat. Conf. Neural Inform. Processing Systems, vol. 29 (Curran Associates Inc., Red Hook, NY), 2216–2224.Google Scholar
- Open Science Collaboration (2015) Estimating the reproducibility of psychological science. Science 349(6251):aac4716.Crossref, Google Scholar
- (2022) Machine learning in information systems research. MIS Quart. 46(1):iii--xix.Google Scholar
- (2009) A survey on transfer learning. IEEE Trans. Knowledge Data Engrg. 22(10):1345–1359.Crossref, Google Scholar
- (2022) A comparison of approaches to improve worst-case predictive model performance over patient subpopulations. Sci. Rep. 12(1):3254.Crossref, Google Scholar
- (2018) The eICU collaborative research database, a freely available multi-center database for critical care research. Sci. Data 5(1):1–13.Crossref, Google Scholar
- (2021) Correcting misclassification bias in regression models with variables generated via data mining. Inform. Systems Res. 32(2):462–480.Link, Google Scholar
- (2025) Correcting measurement error in regression models with variables constructed from aggregated output of data mining models. MIS Quart. 49(1):29–60.Crossref, Google Scholar
- (2020) Learning to learn single domain generalization. Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE Computer Society, Washington, DC), 12556–12565.Google Scholar
- (2019) Distributionally robust optimization: A review. Preprint, submitted August 13, https://arxiv.org/abs/1908.05659.Google Scholar
- (2019) Multi-component image translation for deep domain generalization. 2019 IEEE Winter Conf. Appl. Comput. Vision (IEEE Computer Society, Washington, DC), 579–588.Google Scholar
- (2016) Editor’s comments: Synergies between big data and theory. MIS Quart. 40(2):iii–iix.Crossref, Google Scholar
- (2020) Editor’s comments: Proactively attending to uncertainty in is research. MIS Quart. 44(1):iii–viii.Crossref, Google Scholar
- (2017) Editor’s comments: Diversity of design science research. MIS Quart. 41(1): iii--xviii.Google Scholar
- (2018) Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine 1(1):1–10.Crossref, Google Scholar
- (2020) Distributionally robust neural networks. Proc. 8th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).Google Scholar
- (2020) Learning to optimize domain specific normalization for domain generalization. Vedaldi A, Bischof H, Brox T, JFrahm J-M, eds. Comput. Vision ECCV 2020: 16th Eur. Conf. Proc. Part XXII 16 (Springer, Berlin), 68–83.Google Scholar
- (2025) Examining the impact of generative AI on users’ voluntary knowledge contribution: Evidence from a natural experiment on stack overflow. Inform. Systems Res. Forthcoming.Link, Google Scholar
- (2021) Towards out-of-distribution generalization: A survey. Preprint, submitted Augsut 31, https://arxiv.org/abs/2108.13624v1.Google Scholar
- (2022) Domain generalization—A causal perspective. Preprint, submitted September 30, https://arxiv.org/abs/2209.15177v1.Google Scholar
- (2011) Predictive analytics in information systems research. MIS Quart. 35(3):553–572.Crossref, Google Scholar
- (2023) Distributionally robust batch contextual bandits. Management Sci. 69(10):5772–5793.Link, Google Scholar
- (2020) Targeting prospective customers: Robustness of machine-learning methods to typical data challenges. Management Sci. 66(6):2495–2522.Link, Google Scholar
- (2018) Certifying some distributional robustness with principled adversarial training. 6th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).Google Scholar
- (2016) Deep CORAL: Correlation alignment for deep domain adaptation. Comput. Vision ECCV 2016 Workshops Proc., Part III 14 (Springer, Berlin), 443–450.Google Scholar
- (2008) Visualizing data using t-SNE. J. Machine Learn. Res. 9(11):2579–2605.Google Scholar
- (2014) A systematic review of barriers to data sharing in public health. BMC Public Health 14(1):1–9.Crossref, Google Scholar
- (1991) Principles of risk minimization for learning theory. Moody J, Hanson S, Lippmann RP, eds. Proc. 5th International Conf. Neural Inform. Processing Systems (Morgan Kaufmann Publishers Inc., San Francisco, CA), 831–838.Google Scholar
- (2018) Generalizing to unseen domains via adversarial data augmentation. Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. Proc. 32nd Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 339–5349.Google Scholar
- (2020) Cross-domain face presentation attack detection via multi-domain disentangled representation learning. Proc. 2020 IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE Computer Society, Washington, DC), 6678–6687.Google Scholar
- (2022) Generalizing to unseen domains: A survey on domain generalization. IEEE Trans. Knowledge Data Engrg. 35(8):8052–8072.Google Scholar
- (2016) Breaking down data silos. Harvard Business Review (December 6), https://hbr.org/2016/12/breaking-down-data-silos.Google Scholar
- (2021) Robust and generalizable visual representation learning via random convolutions. Proc. 9th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).Google Scholar
- (2023) Getting personal: A deep learning artifact for text-based measurement of personality. Inform. Systems Res. 34(1):194–222.Link, Google Scholar
- (2018) Mind the gap: Accounting for measurement error and misclassification in variables generated via data mining. Inform. Systems Res. 29(1):4–24.Link, Google Scholar
- (2018) Mixup: Beyond empirical risk minimization. Proc. 6th Internat. Conf. Learn. Representations ((ICLR, Appleton, WI).Google Scholar
- (2023a) Debiasing ML-or AI-generated regressors in partial linear models. Preprint, submitted November 30, https://doi.org/10.2139/ssrn.4636026.Google Scholar
- (2021a) An empirical framework for domain generalization in clinical settings. Proc. Conf. Health Inference Learn. (ACM, New York), 279–290.Google Scholar
- (2023b) Nico++: Towards better benchmarking for domain generalization. Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE Computer Society, Washington, DC), 16036–16047.Google Scholar
- (2021b) Adaptive risk minimization: Learning to adapt to domain shift. Ranzato M, Beygelzimer A, Dauphin Y, Liang, PS, Workman VJ, eds. Proc. 35th Neural Inform. Processing Systems, vol. 34 (Curran Associates Inc., Red Hook, NY), 23664–23678.Google Scholar
- (2021) Domain generalization with mixstyle. Proc. 9th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).Google Scholar
- (2022) Domain generalization: A survey. IEEE Trans. Pattern Anal. Machine Intelligence 45(4):4396–4415.Google Scholar

