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Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data.
A. Wald and J. Wolfowitz, “On a test whether two samples are from the same population,” Ann. Math. Statist. , vol. 11, no. 2, pp. 147–162, 1940
1940
Earlier work this paper cites.
S. Kullback and R. Liebler, “On information and sufficiency,” Ann. Math. Statist. , vol. 22, no. 1, pp. 79–86, 1951
1951
Earlier work this paper cites.
I. J. Good, “The Population Frequencies of Species and the Estimation of Population Parameters,” Biometrika , vol. 40, no. 3/4, 1953
1953
Earlier work this paper cites.
B. Silverman, Density Estimation for Statistics and Data Analysis , ser. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Taylor & Francis, 1986. [Online]. Available: https://books.google.com/books?id=e-xsrjsL7WkC
1986
Earlier work this paper cites.
J. R. Quinlan, “Combining instance-based and model-based learning,” in Proceedings of the tenth international conference on machine learning , 1993, pp. 236–243
1993
Earlier work this paper cites.
P. J. Haas, J. F. Naughton, S. Seshadri, and L. Stokes, “Sampling-based estimation of the number of distinct values of an attribute,” in PVLDB , 1995, pp. 311–322. [Online]. Available: http://www.vldb.org/conf/1995/P311.PDF
1995
Earlier work this paper cites.
T. M. Ha and H. Bunke, “Off-line, handwritten numeral recognition by perturbation method,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 19, no. 5, pp. 535–539, May 1997. [Online]. Available: https://doi.org/10.1109/34.589216
1997
Earlier work this paper cites.
H. Shimodaira, “Improving predictive inference under covariate shift by weighting the log-likelihood function,” Journal of statistical planning and inference , vol. 90, no. 2, pp. 227–244, 2000
2000
Earlier work this paper cites.
M. Steinbach, G. Karypis, V. Kumar et al. , “A comparison of document clustering techniques,” in KDD workshop on text mining , vol. 400, no. 1. Boston, 2000, pp. 525–526
2000
Earlier work this paper cites.
J. L. Schafer and J. W. Graham, “Missing data: our view of the state of the art.” Psychological methods , vol. 7, no. 2, p. 147, 2002
2002
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,” Journal of artificial intelligence research , vol. 16, pp. 321–357, 2002
2002
Earlier work this paper cites.
B. Zadrozny, “Learning and evaluating classifiers under sample selection bias,” in Proceedings of the twenty-first international conference on Machine learning . ACM, 2004, p. 114
2004
Earlier work this paper cites.
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) . Berlin, Heidelberg: Springer-Verlag, 2006
2006
Earlier work this paper cites.
H. Daume III and D. Marcu, “Domain adaptation for statistical classifiers,” Journal of Artificial Intelligence Research , vol. 26, pp. 101–126, 2006
2006
Cited alongside, same era.
S. Bickel, M. Brückner, and T. Scheffer, “Discriminative learning for differing training and test distributions,” in Proceedings of the 24th international conference on Machine learning . ACM, 2007, pp. 81–88
2007
Cited alongside, same era.
J. Huang, A. Gretton, K. M. Borgwardt, B. Schölkopf, and A. J. Smola, “Correcting sample selection bias by unlabeled data,” in Advances in neural information processing systems , 2007, pp. 601–608
2007
Cited alongside, same era.
P.-Y. Hsueh, P. Melville, and V. Sindhwani, “Data quality from crowdsourcing: a study of annotation selection criteria,” in Proceedings of the NAACL HLT 2009 workshop on active learning for natural language processing . Association for Computational Linguistics, 2009, pp. 27–35
2009
Cited alongside, same era.
J. Dowling et al., “Final report: National advisory committee on racial, ethnic, and other populations administrative records, internet, and hard to count population working group,” U.S. Census Bureau, Tech. Rep., 2016. [Online]. Available: www2.census.gov/cac/nac/reports/2016-07-admin_internet-wg-report.pdf
2016
Later among the works it cites.
B. Trushkowsky, T. Kraska, and P. Sarkar, “Answering enumeration queries with the crowd,” Commun. ACM , vol. 59, no. 1, pp. 118–127, 2016. [Online]. Available: http://doi.acm.org/10.1145/2845644
2016
Later among the works it cites.
S. C. Wong, A. Gatt, V. Stamatescu, and M. D. McDonnell, “Understanding data augmentation for classification: when to warp?” in 2016 international conference on digital image computing: techniques and applications (DICTA) . IEEE, 2016, pp. 1–6
2016
Later among the works it cites.
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S. J. Pan, Q. Yang et al. , “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, pp. 1345–1359, 2010
2010
Cited alongside, same era.
J. Attenberg, P. G. Ipeirotis, and F. J. Provost, “Beat the machine: Challenging workers to find the unknown unknowns.” Human Computation , vol. 11, no. 11, pp. 2–7, 2011
2011
Cited alongside, same era.
M. Lease, “On quality control and machine learning in crowdsourcing.” Human Computation , vol. 11, no. 11, 2011
2011
Cited alongside, same era.
D. J. Henderson and C. F. Parmeter, “Normal reference bandwidths for the general order, multivariate kernel density derivative estimator,” Statistics & Probability Letters , vol. 82, no. 12, pp. 2198–2205, 2012
2012
Cited alongside, same era.
M. Sugiyama, M. Yamada, and M. C. du Plessis, “Learning under nonstationarity: covariate shift and class-balance change,” Wiley Interdisciplinary Reviews: Computational Statistics , vol. 5, no. 6, pp. 465–477, 2013
2013
Cited alongside, same era.
Kaggle, “Dogs vs cats dataset,” 2013. [Online]. Available: https://www.kaggle.com/c/dogs-vs-cats/data
2013
Cited alongside, same era.
A. Liu and B. Ziebart, “Robust classification under sample selection bias,” in Advances in neural information processing systems , 2014, pp. 37–45
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2016
Later among the works it cites.
H. Lakkaraju, E. Kamar, R. Caruana, and E. Horvitz, “Identifying unknown unknowns in the open world: Representations and policies for guided exploration.” in AAAI , vol. 1, 2017, p. 2
2017
Later among the works it cites.
M. Sugiyama, N. D. Lawrence, A. Schwaighofer et al. , Dataset shift in machine learning . The MIT Press, 2017
2017
Later among the works it cites.
Y. Chung, S. Krishnan, and T. Kraska, “A data quality metric (dqm): How to estimate the number of undetected errors in data sets,” Proc. VLDB Endow. , vol. 10, no. 10, pp. 1094–1105, Jun. 2017. [Online]. Available: https://doi.org/10.14778/3115404.3115414
2017
Later among the works it cites.
K. Ferryman and M. Pitcan, “Fairness in precision medicine,” Data & Society, Tech. Rep., 2018. [Online]. Available: tinyurl.com/y7q5xnzs
2018
Closest in time.
Wikipedia contributors, “Overfitting — Wikipedia, the free encyclopedia,” 2017, [Online; accessed 22-July-2018]. [Online]. Available: https://en.wikipedia.org/wiki/Overfitting
2018
Closest in time.
G. Bansal and D. S. Weld, “A coverage-based utility model for identifying unknown unknowns,” in Proc. of AAAI , 2018
2018
Closest in time.
Y. Chung, M. L. Mortensen, C. Binnig, and T. Kraska, “Estimating the impact of unknown unknowns on aggregate query results,” ACM Transactions on Database Systems (TODS) , vol. 43, no. 1, p. 3, 2018
2018
Closest in time.
M. Murgia, “Ai’s new workforce: the data-labelling industry spreads globally,” Financial Times, Tech. Rep., 2019. [Online]. Available: https://www.ft.com/content/56dde36c-aa40-11e9-984c-fac8325aaa04
2019
Closest in time.