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We study learning algorithms when there is a mismatch between the distributions of the training and test datasets of a learning algorithm.
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S. Magliacane, T. Van Ommen, T. Claassen, S. Bongers, P. Versteeg, and J. M. Mooij, “Domain adaptation by using causal inference to predict invariant conditional distributions,” Advances in neural information processing systems , vol. 31, 2018
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2021
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G. Aminian, M. Abroshan, M. M. Khalili, L. Toni, and M. Rodrigues, “An information-theoretical approach to semi-supervised learning under covariate-shift,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2022, pp. 7433–7449
2022
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