Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improving …
Conformal prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods …
We study conformal prediction in the one-shot federated learning setting. The main goal is to compute marginally and training-conditionally valid prediction sets, at the server-level, in …
The main objective of this thesis is to increase trust in Machine Learning models by developing tools capable of explaining their predictions and quantifying the associated …
M Kang, Z Lin, J Sun, C Xiao, B Li - arXiv preprint arXiv:2406.01960, 2024 - arxiv.org
Conformal prediction has shown impressive capacity in constructing statistically rigorous prediction sets for machine learning models with exchangeable data samples. The siloed …
S Akansha - arXiv preprint arXiv:2408.13825, 2024 - arxiv.org
Graph Neural Networks (GNNs) have emerged as powerful tools for predicting outcomes in graph-structured data. However, a notable limitation of GNNs is their inability to provide …
S Akansha - arXiv preprint arXiv:2405.11968, 2024 - arxiv.org
Graph Neural Networks (GNNs) have emerged as potent tools for predicting outcomes in graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their …
Q Yin, J Huang, H Yao, L Zhang - arXiv preprint arXiv:2402.16158, 2024 - arxiv.org
As federated learning gains increasing importance in real-world applications due to its capacity for decentralized data training, addressing fairness concerns across demographic …