Ensuring Conditional Independence (CI) constraints is pivotal for the
development of fair and trustworthy machine learning models. In this paper, we
introduce \sys, a framework that harnesses optimal transport theory for data
repair under CI constraints. Optimal transport theory provides a rigorous
framework for measuring the discrepancy between probability distributions,
thereby ensuring control over data utility. We formulate the data repair
problem concerning CIs as a Quadratically Constrained Linear Program (QCLP) and
propose an alternating method for its solution. However, this approach faces
scalability issues due to the computational cost associated with computing
optimal transport distances, such as the Wasserstein distance. To overcome
these scalability challenges, we reframe our problem as a regularized
optimization problem, enabling us to develop an iterative algorithm inspired by
Sinkhorn's matrix scaling algorithm, which efficiently addresses
high-dimensional and large-scale data. Through extensive experiments, we
demonstrate the efficacy and efficiency of our proposed methods, showcasing
their practical utility in real-world data cleaning and preprocessing tasks.
Furthermore, we provide comparisons with traditional approaches, highlighting
the superiority of our techniques in terms of preserving data utility while
ensuring adherence to the desired CI constraints.

我们介绍了一种利用最优输运理论进行条件独立性约束下数据修复的框架，并将该问题转化为正则化的优化问题，提出了一种受 Sinkhorn 算法启发的迭代算法来解决高维大规模数据的计算问题。通过广泛的实验，我们证明了该方法在实际数据清洗和预处理任务中的高效性和实用性，同时与传统方法进行了比较，突出了我们的技术在保持数据效用和确保所需条件独立性约束方面的优越性。

OTClean: 条件独立性违反数据清洗的最优输运方法

OTClean: Data Cleaning for Conditional Independence Violations using  Optimal Transport

The potential harms of the under-representation of minorities in training
data, particularly in multi-modal settings, is a well-recognized concern. While
there has been extensive effort in detecting such under-representation,
resolution has remained a challenge. With recent advancements in generative AI,
large language models and foundation models have emerged as versatile tools
across various domains. In this paper, we propose Chameleon, a system that
efficiently utilizes these tools to augment a data set with a minimal addition
of synthetically generated tuples, in order to enhance the coverage of the
under-represented groups. Our system follows a rejection sampling approach to
ensure the generated tuples have a high quality and follow the underlying
distribution. In order to minimize the rejection chance of the generated
tuples, we propose multiple strategies for providing a guide for the foundation
model. Our experiment results, in addition to confirming the efficiency of our
proposed algorithms, illustrate the effectiveness of our approach, as the
unfairness of the model in a downstream task significantly dropped after data
repair using Chameleon.

我们提出了 Chameleon，这是一个系统，它有效地利用生成 AI 和基础模型，在最小程度上增加合成生成元组的数据集，以增强被少数群体所代表的覆盖范围，并通过数据修复显著降低了模型在下游任务中的不公平性。

变色龙：公平感知多模态数据增强的基础模型，提升少数族裔的覆盖率

Chameleon: Foundation Models for Fairness-aware Multi-modal Data  Augmentation to Enhance Coverage of Minorities

We focus on the problem of unsupervised cell outlier detection and repair in
mixed-type tabular data. Traditional methods are concerned only with detecting
which rows in the dataset are outliers. However, identifying which cells are
corrupted in a specific row is an important problem in practice, and the very
first step towards repairing them. We introduce the Robust Variational
Autoencoder (RVAE), a deep generative model that learns the joint distribution
of the clean data while identifying the outlier cells, allowing their
imputation (repair). RVAE explicitly learns the probability of each cell being
an outlier, balancing different likelihood models in the row outlier score,
making the method suitable for outlier detection in mixed-type datasets. We
show experimentally that not only RVAE performs better than several
state-of-the-art methods in cell outlier detection and repair for tabular data,
but also that is robust against the initial hyper-parameter selection.

本文提出了 Robust Variational Autoencoder (RVAE) 作为一个深度生成模型来解决表格数据中无监督的异常点检测和修复问题，RVAE 能够识别出异常的单元格并进行修复，该方法在混合的表格数据中的异常点检测和修复中表现优异。