Predicting with Proxies: Transfer Learning in High Dimension
Published Online:2 Oct 2020https://doi.org/10.1287/mnsc.2020.3729
References
- (2019) When algorithmic predictions use human-generated data: A bias-aware classification algorithm for breast cancer diagnosis. Inform. Systems Res. 30(1):97–116.Link, Google Scholar
- (2019) Adaptive clinical trial designs with surrogates: When should we bother? Preprint, submitted June 13, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3397464.Google Scholar
- (2011) Hospital readmission as an accountability measure. JAMA 305(5):504–505.Crossref, Google Scholar
- (2017) Leveraging comparables for new product sales forecasting. Working paper.Google Scholar
- (2017) Interpreting predictive models for human-in-the-loop analytics. Working paper, University of Michigan, Ann Arbor.Google Scholar
- (2019) Meta dynamic pricing: Learning across experiments. Preprint, submitted February 28, 2019, https://arxiv.org/abs/1902.10918.Google Scholar
- (2018) Statistical analysis of a low cost method for multiple disease prediction. Statist. Methods Medical Res. 27(8):2312–2328.Crossref, Google Scholar
- (2014) Inference on treatment effects after selection among high-dimensional controls. Rev. Econom. Stud. 81(2):608–650.Crossref, Google Scholar
- (2012) Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica 80(6):2369–2429.Crossref, Google Scholar
- (2009) Simultaneous analysis of LASSO and Dantzig selector. Ann. Statist. 37(4):1705–1732.Crossref, Google Scholar
- (2013) Competing in the age of omnichannel retailing. MIT Sloan Management Rev. (May 21), https://sloanreview.mit.edu/article/competing-in-the-age-of-omnichannel-retailing/.Google Scholar
- (2011) Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Science & Business Media, Heidelberg, Germany).Crossref, Google Scholar
- (2007) The Dantzig selector: Statistical estimation when p is much larger than n. Ann. Statist. 35(6):2313–2351.Crossref, Google Scholar
- (1997) Multitask learning. Machine Learning 28(1):41–75.Crossref, Google Scholar
- (1995) Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20(1):33–61.Google Scholar
- CMS (2018) Readmissions reduction program (hrrp). Accessed October 2, 2018, https://www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/readmissions-reduction-program.html.Google Scholar
- (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. Proc. 25th Internat. Conf. Machine Learn. (ACM), 160–167.Google Scholar
- . (2009) Full accounting of diabetes and pre-diabetes in the US population in 1988–1994 and 2005–2006. Diabetes Care 32(2):287–294.Crossref, Google Scholar
- (2019) Accounting for discrepancies between online and offline product evaluations. Marketing Sci. 38(1):88–106.Link, Google Scholar
- (2019) Learning preferences with side information. Management Sci. 65(7):3131–3149.Link, Google Scholar
- (2001) The Elements of Statistical Learning, vol. 1. (Springer, New York).Google Scholar
- (2014) Leave-one-out cross-validation is risk consistent for LASSO. Machine Learning 97(1-2):65–78.Crossref, Google Scholar
- ICDM (2013) Personalized Expedia hotel searches. Accessed December 28, 2018, https://www.kaggle.com/c/expedia-personalized-sort.Google Scholar
- (2010) A dirty model for multi-task learning. XXX eds. Advances in Neural Information Processing Systems, vol. XX (Curran Associates, Red Hook, NY), 964–972.Google Scholar
- (2017) Personalized risk prediction for type 2 diabetes: The potential of genetic risk scores. Genetics Medicine 19(3):322–329.Crossref, Google Scholar
- (1987) Asymptotic optimality for c_p,c_l, cross-validation and generalized cross-validation: Discrete index set. Ann. Statist. 15(3):958–975.Crossref, Google Scholar
- (1989) Generalized Linear Models, 2nd ed. (Chapman & Hall, London).Crossref, Google Scholar
- (2008) The group LASSO for logistic regression. J. Roy. Statist. Soc. Series B Statist. Methodology 70(1):53–71.Crossref, Google Scholar
- (2009) Ending extra payment for “never events”—stronger incentives for patients’ safety. New England J. Medicine 360(23):2388–2390.Crossref, Google Scholar
- (2017) Does machine learning automate moral hazard and error? Amer. Econom. Rev. 107(5):476–480.Crossref, Google Scholar
- (2009) A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers. XXX eds. Advances in Neural Information Processing Systems, vol. XX (Curran Associates, Red Hook, NY), 1348–1356.Google Scholar
- (2017) Lost in thought—the limits of the human mind and the future of medicine. New England J. Medicine 377(13):1209–1211.Crossref, Google Scholar
- (2010) A survey on transfer learning. IEEE Trans. Knowledge Data Engrg. 22(10):1345–1359.Crossref, Google Scholar
- (1984) Cross-validation of regression models. J. Amer. Statist. Assoc. 79(387):575–583.Crossref, Google Scholar
- (2006) Constructing informative priors using transfer learning. Proc. 23rd Internat. Conf. Machine Learning (ACM), 713–720.Google Scholar
- (1996) Regression shrinkage and selection via the Lasso. J. Royal Statist. Soc. Series B: Methodological 58(1):267–288.Crossref, Google Scholar
- (2004) Optimal aggregation of classifiers in statistical learning. Ann. Statist. 32(1):135–166.Crossref, Google Scholar
- (2011) Long-term benefits from lifestyle interventions for type 2 diabetes prevention: Time to expand the efforts. Diabetes Care 34(Supplement 2):S210–S214.Crossref, Google Scholar
- (2019) High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge University Press, Cambridge, UK).Google Scholar
- (2018) The value of pop-up stores in driving online engagement in platform retailing: Evidence from a large-scale field experiment with Alibaba. Preprint, submitted March 5, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3129506.Google Scholar

