Federated inference with reliable uncertainty quantification over wireless channels via conformal prediction
In this paper, we consider a wireless federated inference scenario in which devices and a
server share a pre-trained machine learning model. The devices communicate statistical …
server share a pre-trained machine learning model. The devices communicate statistical …
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
N Koutsoubis, Y Yilmaz, RP Ramachandran… - arXiv preprint arXiv …, 2024 - arxiv.org
Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements,
particularly in healthcare. Within medical imaging, ML models hold the promise of improving …
particularly in healthcare. Within medical imaging, ML models hold the promise of improving …
Efficient Conformal Prediction under Data Heterogeneity
V Plassier, N Kotelevskii… - International …, 2024 - proceedings.mlr.press
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 …
which is crucial for ensuring the reliability of predictions. However, common CP methods …
Marginal and training-conditional guarantees in one-shot federated conformal prediction
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 …
compute marginally and training-conditionally valid prediction sets, at the server-level, in …
Trustworthy machine learning: explainability and distribution-free uncertainty quantification
SI Amoukou - 2023 - theses.hal.science
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 …
developing tools capable of explaining their predictions and quantifying the associated …
Certifiably Byzantine-Robust Federated Conformal Prediction
Conformal prediction has shown impressive capacity in constructing statistically rigorous
prediction sets for machine learning models with exchangeable data samples. The siloed …
prediction sets for machine learning models with exchangeable data samples. The siloed …
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification
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 …
graph-structured data. However, a notable limitation of GNNs is their inability to provide …
Conditional Shift-Robust Conformal Prediction for Graph Neural Network
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 …
graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their …
Distribution-Free Fair Federated Learning with Small Samples
As federated learning gains increasing importance in real-world applications due to its
capacity for decentralized data training, addressing fairness concerns across demographic …
capacity for decentralized data training, addressing fairness concerns across demographic …