arXiv:2606. 06391v1 Announce Type: cross Abstract: Sharing the financial impact of rare adverse events across a group can soften extreme individual burdens, but any participant made worse off by the arrangement has reason to leave.
Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public policy where safety is a central concern, improving the...
arXiv:2609.17296v1 Announce Type: cross Abstract: Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public poli...
arXiv:2603. 14198v3 Announce Type: replace-cross Abstract: Deploying trustworthy AI systems requires principled uncertainty quantification.
arXiv:2607. 17311v1 Announce Type: cross Abstract: The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems.
arXiv:2601. 17944v2 Announce Type: replace-cross Abstract: We study repeated allocation of shared resources among agents with time-varying demands and capped linear utilities.
arXiv:2606. 00717v1 Announce Type: cross Abstract: Uncertainty quantification is essential in high-stakes machine learning tasks.
arXiv:2509. 22907v2 Announce Type: replace Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models.
arXiv:2608. 06353v1 Announce Type: cross Abstract: We give a formal mechanism design model for the continuous participatory governance of a deployed AI agent.
arXiv:2505. 22108v4 Announce Type: replace-cross Abstract: Background: Federated learning (FL) enables collaborative training of clinical AI models without centralizing patient data, but adoption is limited by privacy concerns, heterogeneous institutional compliance, and resource disparities; standard differential privacy (DP) applies uniform noise to all clients, penalizing well-compliant or under-resourced institutions.
arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.
The paper proposes a distributionally robust federated learning framework that handles both cross-client mixture uncertainty and within-client distributional ambiguity. It constructs a global ambiguity set as a union of local ambiguity sets, allowing client-specific ambiguity radii and a client-wise separable reformulation. The authors provide a high‑probability out‑of‑sample performance guarantee, develop a penalty‑based federated algorithm, prove its convergence under milder conditions, and validate its effectiveness through simulations.
arXiv:2607. 23310v1 Announce Type: cross Abstract: We study an online variant of discrete fair division under generalized assignment budget constraints.