通过提升指数族充分统计量实现公平密度

Fair MP-Boost：公平且可解释的迷你块提升方法

Fair MP-BOOST: Fair and Interpretable Minipatch Boosting

本文提出 Fair MP-Boost，一种通过自适应采样迷你块并学习特征与观测概率来同时提升公平性、准确性和可解释性的随机提升方法。

Individually Fair Gradient Boosting

We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm conv…

Fair Adversarial Gradient Tree Boosting

Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classification algorithms in tabular data, tree boosting outperforms deep learning. For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output $Y$ with gradient tree boosting while minimizing the ability o…

AdaFair: Cumulative Fairness Adaptive Boosting

The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential discrimination. In particular, in certain cases it has been observed that ML algorithms can provide different decisions based on sensitive attributes such as gender or race and therefore can lead to discrimination. Although, several fairness-aware ML approaches have been proposed, their focus has been largely on preserving the overall classification accuracy while improving fairness in predictions for both protected …

FairIF：通过影响函数与验证集敏感属性提升深度学习公平性

FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes

提出两阶段训练算法FAIRIF，利用影响函数和验证集敏感属性计算样本权重，在不修改模型的情况下缓解深度学习中的不公平问题，并保持公平性与效用的良好权衡。

Boosting with the Logistic Loss is Consistent

This manuscript provides optimization guarantees, generalization bounds, and statistical consistency results for AdaBoost variants which replace the exponential loss with the logistic and similar losses (specifically, twice differentiable convex losses which are Lipschitz and tend to zero on one side). The heart of the analysis is to show that, in lieu of explicit regularization and constraints, the structure of the problem is fairly rigidly controlled by the source distribution itself. The first control of this type is in the separable case, where a distribution-dependent relaxed weak learnin…