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cs.LG2026
Factorizable joint shift revisited
Dirk Tasche
Factorizable joint shift (FJS) represents a type of distribution shift (or dataset shift) that comprises both covariate and label shift. Recently, it has been observed that FJS act…
cs.LG2026
Data Pruning: Redundant, Problematic, and Interdependent Samples
Leon Freese, Marthinus W. Theunissen
The performance of deep learning models is affected by not only data quantity but also data quality. Data pruning is a process by which practitioners can reduce the size of a datas…