Unbiased Binning for Fairness-aware A ! ribute Representation

Highly-Efficient Large-Scale 𝑘 -means with Individual Fairness

Function Over Form: Distributional Orthogonalization in Mixture-of-Experts with Replica Expert Mechanism

This paper proposes Distributional Orthogonalization Loss (DO-loss), an auxiliary regularization that shifts the focus from static weight diversity to dynamic routing behavior, and introduces the Replica Expert Mechanism (REM), which improves load balancing through a two-tiered strategy.

Efficient Active Auditing of Multi-Group Fairness with Bias Probes

Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM) while controlling unfairness bias. In practice, however, fairness-aware training often yields limited improvements over standard ERM, making reliable post hoc auditing es...

Parameterized Approximation Schemes for Fair Clustering in Doubling Metrics

This paper focuses on the k-center problem in bounded doubling metrics under two popular fairness requirements: group fairness and data summarization fairness, referred to Group Fair k-Center (Gf-k-Cen) and Data Summarization Fair k-Center (Dsf-k-Cen), respectively.

Advantage Scale Calibration Imbalance in Group-Relative Optimization under Low-Variance Rewards: Diagnosis and Bounded Recovery

An advantage-scale three-way calibration interface is proposed: the same within-group scale denominator simultaneously determines the reward-branch strength, prompt-level batch weight, and the effective KL calibration induced when the reward branch is re-expressed on the original cardinal scale.

A Redundancy Reduction Approach for Controllable Sequential Recommendations

This work proposes a decorrelation-regularized training framework that augments next-item prediction with an auxiliary redundancy-reduction term, and instantiate it with BT-SR, which uses the Barlow Twins objective to form label-consistent positive pairs without synthetic corruptions.