在端到端机器学习管道中,对表格数据的实用性和公平性进行差异化私有合成数据的评估
Mayana Pereira1,2, Meghana Kshirsagar1, Sumit Mukherjee3
1AI for Good Research Lab, Microsoft, Redmond, Washington, United States of America.
PloS one
|February 5, 2024
概括
基于边际的合成数据生成器比基于GAN的生成器更有效地在表格数据上训练机器学习模型,实现与真实数据相似的实用性和公平性.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 不同的私有 (DP) 合成数据为数据共享提供了一个保护隐私的解决方案.
- 由于数据稀缺和隐私法规,其在医疗保健等敏感领域的应用至关重要.
- 了解DP合成数据对机器学习管道的影响至关重要.
研究的目的:
- 调查DP合成数据在取代真实表格数据的机器学习中的有效性.
- 确定最佳的合成数据生成技术,用于模型培训和评估.
- 分析DP合成数据对下游分类任务的实用性和公平性影响.
主要方法:
- 基于边际和基于GAN的合成数据生成算法的系统研究.
- 开发一种用于培训和评估ML模型的新框架,而不需要实际测试数据.
- 综合分析包括多个公平性定义.
主要成果:
- 基于边际的合成数据生成器在表格数据的模型训练实用程序中优于基于GAN的方法.
- 用边际基合成数据训练的模型表现出与用真实数据训练的模型相比的实用性.
- AIM和MWEM PGM算法生成合成数据,有效地平衡实用性和公平性.
结论:
- 基于边际的合成数据生成是一种可行的和有效的替代方案,用于表式机器学习的真实数据.
- 合成DP数据,特别是来自AIM和MWEM PGM的合成数据,可以实现高效率和公平性.
- 这项研究为评估隐私敏感应用中的合成数据提供了强大的框架.
相关概念视频
01:15Data Validation
162
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
162
01:09Censoring Survival Data
95
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
95
01:22Bias
4.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.2K
01:17Wald-Wolfowitz Runs Test I
648
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
The test works...
648
01:15One-Way ANOVA: Equal Sample Sizes
3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
130
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
130