Method for outlier robust subgroup inference via clustering in the gradient space

Patent metadata

Field	Value
Publication number	US-2024362534-A1
Application number	US-202318309755-A
Country	US
Kind code	A1
Filing date	Apr 28, 2023
Priority date	Apr 28, 2023
Publication date	Oct 31, 2024
Grant date	—

Abstract

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.

First claim

What is claimed is:

1 . A computer-implemented method for identifying relevant subgroups in a training dataset associated with a machine learning model, comprising:
receiving a classification dataset wherein subgroups are unlabeled; for each data point in the classification dataset, using 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; clustering the gradient representations to provide estimated subgroup labels; and outputting cluster assignments as the estimated subgroup labels.

2 . The computer-implemented method of claim 1 , further comprising using an outlier-robust clustering algorithm to perform the clustering of the gradient representations.

3 . The computer-implemented method of claim 1 , wherein classes are labeled in the classification dataset.

4 . The computer-implemented method of claim 1 , further comprising learning group annotations and identifying outliers of the classification dataset.

5 . The computer-implemented method of claim 1 , further comprising training a robust classifier using the estimated subgroup labels.

6 . The computer-implemented method of claim 5 , further comprising applying distributionally robust optimization (DRO) to train the robust classifier.

7 . The computer-implemented method of claim 1 , further comprising, in response to receiving the classification dataset, applying a non-robust neural network classifier, wherein a last layer representation of the non-robust neural network classifier is extracted as dimension-reduced features.

8 . The computer-implemented method of claim 7 , wherein the gradient space partitioning is performed on the last-layer representation.

9 . A computer-implemented method for identifying relevant subgroups, in a presence of outliers, for training a classifier to be robust to the identified subgroups, comprising:
receiving a classification dataset wherein subgroups are unlabeled; for each data point in the classification dataset, using 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, wherein the GraSP further learns group annotations and identify outliers; clustering the gradient representations to estimate subgroup labels, wherein clustering further comprises using an outlier-robust clustering algorithm to cluster the gradient representations; outputting cluster assignments as the estimated subgroup labels; and training a robust classifier using the estimated subgroup labels.

10 . The computer-implemented method of claim 9 , wherein classes are labeled in the classification dataset.

11 . The computer-implemented method of claim 9 , further comprising applying distributionally robust optimization (DRO) to train the robust classifier.

12 . The computer-implemented method of claim 9 , further comprising, in response to receiving the classification dataset, applying a non-robust neural network classifier, wherein a last layer representation of the non-robust neural network classifier is extracted as dimension-reduced features.

13 . The computer-implemented method of claim 12 , wherein the gradient space partitioning is performed on the last-layer representation.

14 . A non-transitory computer readable storage medium tangibly embodying a computer readable program code having computer readable instructions that, when executed, causes a computer device to carry out a method of identifying relevant subgroups in a training dataset associated with a machine learning model, the method comprising:
receiving a classification dataset wherein subgroups are unlabeled; for each data point in the classification dataset, using 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; clustering the gradient representations to provide estimated subgroup labels; and outputting cluster assignments as the estimated subgroup labels.

15 . The non-transitory computer readable storage medium of claim 14 , the method further comprising using an outlier-robust clustering algorithm to perform the clustering of the gradient representations.

16 . The non-transitory computer readable storage medium of claim 14 , the method further comprising learning group annotations and identifying outliers of the classification dataset.

17 . The non-transitory computer readable storage medium of claim 14 , the method further comprising training a robust classifier using the estimated subgroup labels.

18 . The non-transitory computer readable storage medium of claim 17 , the method further comprising applying distributionally robust optimization (DRO) to train the robust classifier.

19 . The non-transitory computer readable storage medium of claim 14 , the method further comprising, in response to receiving the classification dataset, applying a non-robust neural network classifier, wherein a last layer representation of the non-robust neural network classifier is extracted as dimension-reduced features.

20 . The non-transitory computer readable storage medium of claim 19 , wherein the gradient space partitioning is performed on the last-layer representation.

Assignees

• IBM
• Massachusetts Inst Technology

Inventors

• Greenewald Kristjan Herbert
• Jung Luann
• Solomon Justin
• Yurochkin Mikhail
• Zeng Yuchen

Classifications

• G06N3/08

  Learning methods · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N20/00 Primary

  Machine learning · CPC title