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.
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.
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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 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 AI
6d ago
Confidence Calibration of Deep Learning Systems
arXiv:2608. 12100v1 Announce Type: cross Abstract: In high-stakes applications, reliable confidence estimates are as important as the predictions themselves.
arXiv Machine Learning
Jul 10
Multi-Distribution Robust Conformal Prediction
arXiv:2601. 02998v2 Announce Type: replace Abstract: In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them.
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
Parameter-Free and Group Conditional Online Conformal Prediction
arXiv:2606. 00419v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.
arXiv AI
Jul 28
Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View
arXiv:2607. 23029v1 Announce Type: cross Abstract: Federated learning enables collaborative model training across distributed clients without centralising their data, yet privacy remains a persistent concern because the shared model updates can leak information about local datasets.
arXiv Machine Learning
Jul 10
Conformal Predictive Programming for Chance Constrained Optimization
arXiv:2402. 07407v3 Announce Type: replace-cross Abstract: We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i.
arXiv Machine Learning
Jul 21
Isotonic Conformal Prediction
arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.
Hugging Face Trending Papers
Aug 3
Private Generative Bootstrap via Blocking
With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers. Equally important is to privatize the reporting of uncertainty in such answers.
arXiv Machine Learning
Jun 11
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.