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stat.ML2024
Optimizing importance weighting in the presence of sub-population shifts
Floris Holstege, Bram Wouters, Noud van Giersbergen +1
A distribution shift between the training and test data can severely harm performance of machine learning models. Importance weighting addresses this issue by assigning different w…
stat.ML2019
A Bayesian Approach for Accurate Classification-Based Aggregates
Q. A. Meertens, C. G. H. Diks, H. J. van den Herik +1
In this paper, we study the accuracy of values aggregated over classes predicted by a classification algorithm. The problem is that the resulting aggregates (e.g., sums of a variab…