Associate Professor, Nanjing University
3 papers at NeurIPS 2025
We propose a new error metric for constructing coresets in \$(k,z)\$-clustering with noisy data, leading to smaller coresets, stronger theoretical guarantees, and improved empirical performance compared to classical methods.
We show how perceived post-selection bias distorts strategic effort in merit-based selection, leading to disparities. Our model quantifies interventions to reduce inequity by adjusting selectivity and perceived valuation gaps.
