arXiv Machine Learning
Optimally Selecting Representative Agents from a Metric Space
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arXiv AI
Oct 23
Allocating Indivisible Goods to Strategic Agents: Pure Nash Equilibria and Fairness
The paper studies how to allocate indivisible goods to strategic agents with additive valuations without monetary transfers. It investigates whether mechanisms can guarantee pure Nash equilibria while ensuring fairness, focusing on envy‑freeness up to one good (EF1) and envy‑freeness up to any good (EFX). The authors show that the Round‑Robin algorithm yields EF1 allocations at every pure Nash equilibrium, and that a cut‑and‑choose algorithm for two agents produces EFX and maximin share fair allocations in equilibrium, a property absent in the non‑strategic setting.
arXiv Machine Learning
Sep 10
A Sub-4 Approximation for Fair $k$-Means
arXiv:2609.07974v1 Announce Type: cross Abstract: Fairness in clustering has attracted sustained research interest, motivated by the need to ensure equitable representation of protected groups in mac...
arXiv AI
Oct 15
Improving the Bounds on Approximating Weighted Maximin Share for Chores
arXiv:2610.15520v1 Announce Type: cross Abstract: We investigate the problem of allocating indivisible chores among agents with arbitrary positive weights and additive nonnegative cost functions. An...
arXiv AI
2d ago
Learning Lotteries with Minimal Stakeholder Violations from Membership Queries
arXiv:2610. 37828v1 Announce Type: cross Abstract: An AI developer often has several candidate versions of a system and can deploy a mixture of them, sending each request to a randomly chosen version, as in A/B testing.
arXiv Machine Learning
Aug 14
Online Correlation Clustering: Simultaneously Optimizing All $\ell_p$-norms
arXiv:2510. 15076v2 Announce Type: replace Abstract: The $\ell_p$-norm objectives for correlation clustering present a fundamental trade-off between minimizing total disagreements (the $\ell_1$-norm) and ensuring fairness to individual nodes (the $\ell_\infty$-norm).
arXiv Machine Learning
Aug 10
Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.