Bayesian Learning
Differentially Private Statistical Inference through ฮฒ-Divergence One Posterior Sampling Jack Jewson
Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process.
Learning Causal Models under Independent Changes
In many scientific applications, we observe a system in different conditions in which its components may change, rather than in isolation.
A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
Federated learning (FL), through its privacy-preserving collaborative learning approach, has significantly empowered decentralized devices.
Implicit Variational Inference for High-Dimensional Posteriors
In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex multimodal and correlated posteriors in high-dimensional spaces.
Bayesian Learning via Q-Exponential Process
Regularization is one of the most fundamental topics in optimization, statistics and machine learning.