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Efficient Conformal Prediction under Data Heterogeneity
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions.
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized.
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms
This paper focuses on Bayesian inference in a federated learning context (FL).
Membership Inference Attacks via Adversarial Examples
In this paper, we develop a mean to measure the leakage of training data leveraging a quantity appearing as a proxy of the total variation of a trained model near its training samples.
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning.
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
The objective of Federated Learning (FL) is to perform statistical inference for data which are decentralised and stored locally on networked clients.
Risk bounds when learning infinitely many response functions by ordinary linear regression
Consider the problem of learning a large number of response functions simultaneously based on the same input variables.
