importance weighting

This paper addresses the problem of biased predictions by machine learning models against marginalized groups in real-world data. To jointly optimize predictive accuracy and fairness, we propose a genetic algorithm-based sample weighting method that evolves instance-level weights through multi-objective optimization. Unlike conventional uniform or feature-driven weighting schemes, our approach simultaneously optimizes accuracy, AUC, demographic parity difference, and subgroup false negative rate. Extensive experiments on 11 publicly available datasets—including two healthcare benchmarks—demonstrate that the evolved weights substantially improve the fairness–performance trade-off. The most significant gains are achieved when jointly optimizing for accuracy and demographic parity difference, confirming the method’s effectiveness and generalizability in practical, high-stakes domains.

Machine learning models exhibit high sensitivity to minor perturbations in training data, leading to unstable predictions; yet conventional fairness metrics (e.g., bias-based indicators) ignore this prediction uncertainty. Method: We propose a variance-oriented paradigm for group fairness—introducing the first systematic framework that treats inter-group predictive variance equality as a core fairness criterion, grounded in statistical error decomposition and theoretical analysis of variance’s independent impact on fairness assessment. Contribution/Results: We release VarFair, the first open-source library integrating uncertainty quantification with fairness evaluation. Extensive experiments on Adult, COMPAS, and other benchmarks demonstrate that groups with high predictive variance are frequently misclassified as “fair” by standard methods, whereas our variance-aware metric significantly improves identification of disadvantaged groups and enhances assessment robustness under data perturbations.

Conventional resampling methods for class-imbalanced classification suffer from inherent limitations—oversampling introduces noise and boundary ambiguity, while undersampling discards informative majority-class samples, leading to information loss and underfitting. Method: This paper proposes an intelligent majority-class sample selection mechanism guided by model loss improvement. Its core innovation is a novel gradient-driven, differentiable bilevel optimization framework: the upper-level objective maximizes generalization performance, while the lower-level optimizes a differentiable loss improvement metric, enabling end-to-end, deterministic undersampling. Contribution/Results: By directly selecting discriminative majority-class instances—without synthesizing noisy minority samples—the method preserves data fidelity and decision boundary clarity. Evaluated on multiple benchmark datasets, it achieves up to a 10% absolute improvement in F1-score over state-of-the-art methods, significantly enhancing minority-class detection while maintaining majority-class accuracy.

To address the challenge of sustaining fairness auditing for deployed models under data policy drift, subgroup heterogeneity, and dynamic distributional shifts, this paper proposes the first dynamic fairness auditing framework tailored for streaming data. Methodologically, it innovatively adapts “testing by betting”—a game-theoretic statistical paradigm—into fairness monitoring, integrating anytime-valid inference with nonparametric, sample-size-agnostic sequential testing that adapts to time-varying sampling strategies. Unlike conventional batch-based approaches, our framework enables real-time, continuous fairness assessment without requiring prior knowledge of data distribution or fixed sample sizes. Experiments on three benchmark datasets demonstrate that the method achieves high detection sensitivity, low false positive rate, and strong robustness against concept drift and subgroup imbalance. These properties collectively enhance the practicality, timeliness, and deployability of fairness monitoring in real-world operational environments.

This work addresses the problem of user-controllable fairness tuning in regression models. We propose a general fairness framework built upon ridge regression penalization. Its core innovation lies in explicitly incorporating fairness constraints into the ridge parameter selection process—achieving an adjustable trade-off between fairness and predictive performance via regularization with respect to sensitive attributes—and deriving partial closed-form solutions. The method supports multiple fairness definitions (e.g., demographic parity, equalized odds), extends to generalized linear models and kernelized settings, and corrects systematic experimental biases present in prior studies. Extensive experiments on six benchmark datasets demonstrate that, at comparable fairness levels, our approach significantly outperforms mainstream baselines—including Komiyama et al. and Zafar et al.—while simultaneously improving both goodness-of-fit and prediction accuracy.

This study addresses the challenge in randomized experiments where conventional sample allocation methods fail to simultaneously account for deployment relevance and statistical precision for a target population. The authors propose TWNA, a two-stage stratified design: an initial pilot stage estimates stratum-specific treatment effect variances, which then inform a joint optimization of final-stage sample sizes and treatment probabilities to enhance estimation precision of the target-weighted group average treatment effect (GATE). TWNA is the first method to unify deployment weights and statistical difficulty within a single optimization framework, yielding a closed-form optimal allocation rule. It further extends robustly to complex settings involving uncertainty in target population composition, skewed outcomes, or rare events. Simulations and empirical analyses demonstrate that TWNA substantially improves estimation accuracy and resource efficiency, particularly for critical yet hard-to-estimate subgroups.

This work addresses the apparent trade-off between prediction accuracy and group fairness commonly observed in existing literature, which often stems from distorted subgroup proportions in training data and leads to misleading assessments of fairness interventions. The authors propose a two-stage paradigm under settings where subgroup distributions are stable but their prevalence is shifted: first, apply a finite-sample optimal shrinkage reweighting method to correct representation bias, then evaluate fairness interventions. This approach integrates importance weighting with shrinkage estimation to decouple spurious trade-offs from genuine ones. Experiments on real-world benchmarks such as Adult and COMPAS, as well as synthetic datasets, validate the theoretical claims and accurately recover the true Pareto frontier between fairness and utility.

This work addresses the challenge of mixed distributional shift, which simultaneously involves systematic bias and random perturbations—a setting where conventional reweighting methods struggle to balance bias correction with uncertainty quantification. The authors propose a novel framework that explicitly disentangles these two sources of shift: systematic bias is corrected via Augmented Inverse Distance Weighting (AIDW) and Augmented Inverse Hybrid Weighting (AIHW), while residual random perturbations are modeled as distributional uncertainty. Robust inference is achieved through variance-optimal data pooling. The method incorporates distributional distance to govern the bias–variance trade-off, offering both asymptotic theoretical guarantees and practical guidance for hyperparameter tuning. Evaluated on three real-world multicenter datasets, the approach substantially reduces mean squared error and improves empirical coverage, demonstrating particular robustness in scenarios where covariate shift correction typically underestimates uncertainty.

This work addresses the challenge that existing model evaluation methods often fail to reliably assess estimator quality in low-variance settings due to confounding between bias and variance or excessive sensitivity of statistical tests. To overcome this limitation, the authors propose a fault-tolerant evaluation framework that unifies bias and variance modeling through an adjustable tolerance parameter ε, enabling robust assessment of sample-efficient performance estimators within practically acceptable error margins. The framework integrates bias-variance analysis, fault-tolerant evaluation theory, and an adaptive ε-optimization algorithm, making it particularly well-suited for scenarios with low annotation costs. Experimental results demonstrate that the proposed approach provides a more comprehensive and reliable characterization of estimator behavior, significantly enhancing both the practical utility and stability of performance evaluation.

This study addresses the interpretational ambiguity of weighted estimators when treatment effects are heterogeneous, as their validity hinges critically on the choice of weights. To tackle this issue, the authors propose an estimator that minimizes worst-case bias and construct confidence intervals that are uniformly valid over a broad class of weighting schemes. Their approach integrates minimax bias reduction, bounds from heterogeneity-robust sensitivity analysis, and theoretical characterizations of discrepancies among weighted estimators, thereby enabling inference robust to weight uncertainty. Empirical applications illustrate the method’s utility: in Lakdawala et al.’s event study, findings remain robust across a wide range of weights, whereas in the Project STAR experiment, conclusions prove sensitive even to minor perturbations of baseline weights.