Wang Zhang, Tsui-Wei Weng, et al.
ICML 2023
This paper addresses a regression problem in which output label values are the results of sensing the magnitude of a phenomenon. A low value of such labels can mean either that the actual magnitude of the phenomenon was low or that the sensor made an incomplete observation. This leads to a bias toward lower values in labels and the resultant learning because labels may have lower values due to incomplete observations, even if the actual magnitude of the phenomenon was high. Moreover, because an incomplete observation does not provide any tags indicating incompleteness, we cannot eliminate or impute them. To address this issue, we propose a learning algorithm that explicitly models incomplete observations corrupted with an asymmetric noise that always has a negative value. We show that our algorithm is unbiased as if it were learned from uncorrupted data that does not involve incomplete observations. We demonstrate the advantages of our algorithm through numerical experiments.
Wang Zhang, Tsui-Wei Weng, et al.
ICML 2023
Minhao Cheng, Rui Min, et al.
ICML 2023
Hazar Yueksel, Ramon Bertran, et al.
MLSys 2020
Takayuki Osogami, T. Imamichi, et al.
IBM J. Res. Dev