Tabular data generation

Patent metadata

Field	Value
Publication number	US-2025124220-A1
Application number	US-202418911044-A
Country	US
Kind code	A1
Filing date	Oct 9, 2024
Priority date	Oct 11, 2023
Publication date	Apr 17, 2025
Grant date	—

Abstract

A tabular data model, which may be pre-trained on a different data set, is used to generate data samples for a target class with a given set of context data points. The tabular data model is trained to predict class membership of a given data point with a set of context data points. Rather than use the predicted class directly, the class predictions are used to determine a class-conditional energy for a synthetic data point with respect to the target class. The synthetic data point may then be updated based on the class-conditional energy with a stochastic update algorithm, such as stochastic gradient Langevin dynamics or Adaptive Moment Estimation with noise. The value of the synthetic data point is sampled as a data point for the target class. This permits effective data augmentation for tabular data for downstream models.

First claim

What is claimed is:

1 . A system for generating synthetic data points, comprising:
a processor configured to execute instructions; a computer-readable medium having instructions executable by the processor for:
identifying a synthetic data point of tabular data;
updating the synthetic data point with respect to a set of context data points by:
determining a class-conditional energy of a target class for the synthetic data point applied to a pre-trained tabular classification model with respect to the set of context data points;
stochastically updating the synthetic data point based on the class-conditional energy of the target class; and

sampling the synthetic data point as a generated data point for the target class.

2 . The system of claim 1 , wherein the pre-trained tabular classification model is not trained on the set of context data points.

3 . The system of claim 1 , wherein identifying the synthetic data point comprises sampling from a distribution based a subset of the context data points having the target class.

4 . The system of claim 1 , wherein the set of context data points include a first subset of context data points associated with the target class and a second subset of context data points associated with at least one other class differing from the target class.

5 . The system of claim 1 , wherein the instructions are further executable for:
training an application computer model with training data that includes the generated data point and one or more data points from the set of context data points.

6 . The system of claim 1 , wherein the class-conditional energy includes a term based on the energy of the set of context data points given the respective class of the context data points.

7 . The system of claim 1 , wherein stochastically updating the synthetic data point based on the class-conditional energy of the target class comprises applying stochastic gradient Langevin dynamics.

8 . The system of claim 1 , wherein stochastically updating the synthetic data point based on the class-conditional energy of the target class comprises applying Adaptive Moment Estimation (Adam) with noise.

9 . A method for generating synthetic, the method comprising:
identifying a synthetic data point of tabular data; updating the synthetic data point with respect to a set of context data points by:
determining a class-conditional energy of a target class for the synthetic data point applied to a pre-trained tabular classification model with respect to the set of context data points;
stochastically updating the synthetic data point based on the class-conditional energy of the target class; and
sampling the synthetic data point as a generated data point for the target class.

10 . The method of claim 9 , wherein the pre-trained tabular classification model is not trained on the set of context data points.

11 . The method of claim 9 , wherein identifying the synthetic data point comprises sampling from a distribution based a subset of the context data points having the target class.

12 . The method of claim 9 , wherein the set of context data points include a first subset of context data points associated with the target class and a second subset of context data points associated with at least one other class differing from the target class.

13 . The method of claim 9 , wherein the method further comprises:
training an application computer model with training data that includes the generated data point and one or more data points from the set of context data points.

14 . The method of claim 9 , wherein the class-conditional energy includes a term based on the energy of the set of context data points given the respective class of the context data points.

15 . The method of claim 9 , wherein stochastically updating the synthetic data point based on the class-conditional energy of the target class comprises applying stochastic gradient Langevin dynamics.

16 . The method of claim 9 , wherein stochastically updating the synthetic data point based on the class-conditional energy of the target class comprises applying Adaptive Moment Estimation (Adam) with noise.

17 . A non-transitory computer-readable medium, the non-transitory computer-readable medium comprising instructions executable by a processor for:
identifying a synthetic data point of tabular data; updating the synthetic data point with respect to a set of context data points by:
determining a class-conditional energy of a target class for the synthetic data point applied to a pre-trained tabular classification model with respect to the set of context data points;
stochastically updating the synthetic data point based on the class-conditional energy of the target class; and
sampling the synthetic data point as a generated data point for the target class.

18 . The computer-readable medium of claim 17 , wherein the instructions are further executable for:
training an application computer model with training data that includes the generated data point and one or more data points from the set of context data points.

19 . The computer-readable medium of claim 17 , wherein stochastically updating the synthetic data point based on the class-conditional energy of the target class comprises applying stochastic gradient Langevin dynamics.

20 . The computer-readable medium of claim 17 , wherein stochastically updating the synthetic data point based on the class-conditional energy of the target class comprises applying Adaptive Moment Estimation (Adam) with noise.

Assignees

• Toronto Dominion Bank

Inventors

• Yu Guangwei
• Ma Junwei
• Caterini Anthony Lawrence
• Stein George Frazer

Classifications

• G06F40/177 Primary

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