arXiv:2609. 29958v1 Announce Type: cross Abstract: We study a matching mechanism where agents and objects are described by features rather than complete rankings.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2506. 03802v2 Announce Type: replace Abstract: We introduce a learning problem in a generalized two-sided matching market, where agents select actions to interact with their match.
arXiv:2607. 04824v1 Announce Type: new Abstract: We study a sequential learning problem for stable matchings in two-sided markets where preferences on both sides are initially unknown.
arXiv:2606. 29221v1 Announce Type: new Abstract: We address the problem of online multi-human multi-robot teaming through the lens of a linear matching bandit framework, where a learner assigns robots with unknown features from a fixed pool to distinct sets of human agents over multiple rounds.
The paper investigates welfare‑maximizing allocation of heterogeneous objects when agents use costly effort for screening instead of monetary transfers. It shows that as the number of object types increases, no‑screening mechanisms become more efficient, reducing the need for screening. The authors prove that in a symmetric continuous market with i.i.d. log‑concave values, the multidimensional allocation problem collapses to a single‑dimensional one based on agents’ best‑option values, and they demonstrate that this no‑screening optimality persists even as variety expands, supported by large‑variety limits and numerical experiments. The findings are applied to design an invitation‑based vaccine appointment system.
The paper introduces a new online fair division framework where a learner must allocate indivisible items to agents in real time, balancing fairness and efficiency. Traditional methods rely on many copies of each item to estimate utilities, but this is unrealistic for platforms with many users and few interactions. By treating utility as an unknown function of item-agent features and framing the problem as a contextual bandit, the authors propose algorithms that achieve sublinear regret and demonstrate their effectiveness experimentally.
The paper introduces “SNSW-Alg”, an algorithm that finds a stable matching maximizing Nash social welfare in the stable marriage problem. It runs in ×O(n^4) time and balances equity while maintaining stability. Experiments across various preference distributions show significant fairness gains with minimal impact on regret, egalitarian criterion, and sex equality, and the resulting matchings are statistically Pareto-undominated by other fairness-based stable matchings.