Research map / Learning theory and algorithms / Submodular and game algorithms
Individually Fair Diversity Maximization
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Studies diversity maximization under individual fairness, requiring every point to have a representative among its n/k nearest neighbors in the selected subset.
Problem
Selecting a size-k subset of n metric-space points that is both maximally diverse and individually fair, meaning every point has one of its n/k nearest neighbors in the subset as representative.
Approach
Formulates diversity maximization with a per-point individual fairness constraint based on nearest-neighbor representatives instead of group-level fairness constraints.
Closest papers by meaning
- Diversity Maximization: Algorithms for Distant $k$-Subsets (NeurIPS 2026 · similarity 0.91)
- Linear Relaxations for Finding Diverse Elements in Metric Spaces (NeurIPS 2016 · similarity 0.90)
- Diversity Maximization in the Presence of Outliers (AAAI 2023 · similarity 0.89)
- Measures of diversity and space-filling designs for categorical data (ICML 2024 · similarity 0.87)
- Individual Fairness in Graph Decomposition (ICML 2024 · similarity 0.85)
- Fair Submodular Cover (ICLR 2025 · similarity 0.85)
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