Robust Non-Linear Correlations via Polynomial Regression
This provides a more reliable computational method for HGR estimation, which is important for applications like algorithmic fairness and causal discovery, though it appears incremental as an improvement over existing differentiable estimation techniques.
The paper tackles the problem of estimating the non-linear Hirschfeld-Gebelein-Rényi (HGR) correlation coefficient robustly for use as a loss regularizer in machine learning, introducing a polynomial kernel-based approach that offers greater robustness and determinism compared to previous methods.
The Hirschfeld-Gebelein-Rényi (HGR) correlation coefficient is an extension of Pearson's correlation that is not limited to linear correlations, with potential applications in algorithmic fairness, scientific analysis, and causal discovery. Recently, novel algorithms to estimate HGR in a differentiable manner have been proposed to facilitate its use as a loss regularizer in constrained machine learning applications. However, the inherent uncomputability of HGR requires a bias-variance trade-off, which can possibly compromise the robustness of the proposed methods, hence raising technical concerns if applied in real-world scenarios. We introduce a novel computational approach for HGR that relies on user-configurable polynomial kernels, offering greater robustness compared to previous methods and featuring a faster yet almost equally effective restriction. Our approach provides significant advantages in terms of robustness and determinism, making it a more reliable option for real-world applications. Moreover, we present a brief experimental analysis to validate the applicability of our approach within a constrained machine learning framework, showing that its computation yields an insightful subgradient that can serve as a loss regularizer.