arXiv AI
Jul 3
Efficient Federated Conformal Prediction with Group-Conditional Guarantee
arXiv:2603. 14198v3 Announce Type: replace-cross Abstract: Deploying trustworthy AI systems requires principled uncertainty quantification.
Conformal Risk Sharing: Certified Cost Allocation with Participation Guarantees
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.
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arXiv AI
Jul 21
The Optimization Trilemma: Efficiency, Comfort and Fairness in Decentralized Multi-agent Coordination
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 AI
1d ago
Credit Fairness: Online Fairness In Shared Resource Pools
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 AI
Jun 2
Multi-Agent Conformal Prediction with Personalized Statistical Validity
arXiv:2606. 00717v1 Announce Type: cross Abstract: Uncertainty quantification is essential in high-stakes machine learning tasks.
arXiv Machine Learning
Jun 2
FedCF: Fair Federated Conformal Prediction
arXiv:2509. 22907v2 Announce Type: replace Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models.
arXiv AI
Jul 15
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
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 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.
arXiv AI
Jul 28
Online Fair Division with Budget Constraints
arXiv:2607. 23310v1 Announce Type: cross Abstract: We study an online variant of discrete fair division under generalized assignment budget constraints.
arXiv Machine Learning
Jun 18
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.
arXiv Machine Learning
Jun 24
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
arXiv:2307. 13127v3 Announce Type: replace-cross Abstract: Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information.
arXiv Machine Learning
Jun 2
Beyond Procedure: Substantive Fairness in Conformal Prediction
arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.
arXiv AI
Jul 7
Efficient Decentralized Multi-task Dataset Valuation via Model Merging
arXiv:2607. 03346v1 Announce Type: cross Abstract: Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models.