发明公开
- 专利标题: METHOD FOR OUTLIER ROBUST SUBGROUP INFERENCE VIA CLUSTERING IN THE GRADIENT SPACE
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申请号: US18309755申请日: 2023-04-28
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公开(公告)号: US20240362534A1公开(公告)日: 2024-10-31
- 发明人: Kristjan Herbert Greenewald , Luann Jung , Justin Solomon , Mikhail Yurochkin , Yuchen Zeng
- 申请人: INTERNATIONAL BUSINESS MACHINES CORPORATION , MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- 申请人地址: US NY ARMONK
- 专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION,MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- 当前专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION,MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- 当前专利权人地址: US NY ARMONK
- 主分类号: G06N20/00
- IPC分类号: G06N20/00
摘要:
A computer-implemented method for identifying relevant subgroups, which are relevant for training a subgroup-robust classifier, in a training dataset associated with a machine learning model includes receiving a classification dataset wherein subgroups are unlabeled. For each data point in the classification dataset, the method uses gradient space partitioning (GraSP) to identify a gradient representation of each data point by extracting an associated gradient of a logistic regression classification loss with respect to weights of a logistic regression. The gradient representations are clustered to provide estimated subgroup labels the cluster assignments are output as the estimated subgroup labels.
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