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A key challenge of modern machine learning systems is to achieve Out-of-Distribution (OOD) generalization -- generalizing to target data whose distribution differs from that of source data.
Mathematical methods of statistics
Harald Cramér · 1946
Earlier work this paper cites.
Maximum likelihood estimation of misspecified models
Halbert White · 1982
Earlier work this paper cites.
Information and the accuracy attainable in the estimation of statistical parameters
C. Radhakrishna Rao · 1992
Earlier work this paper cites.
Applications of the van trees inequality: a bayesian cramér-rao bound
Richard D Gill and Boris Y Levit · 1995
Earlier work this paper cites.
An overview of statistical learning theory
Vladimir N Vapnik · 1999
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
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Earlier work this paper cites.
Self-concordant analysis for logistic regression
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Learning bounds for importance weighting
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Earlier work this paper cites.
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Earlier work this paper cites.
Domain adaptation and sample bias correction theory and algorithm for regression
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Importance sampling: intrinsic dimension and computational cost
S. Agapiou, O. Papaspiliopoulos, D. Sanz-Alonso, and A. M. Stuart · 2017
Earlier work this paper cites.
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Advanced statistical theory lecture 13: February 26, February 2018
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