Bayesian Inference
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time
Diffusion-based generative models, as first introduced by Sohl-Dickstein et al. (2015), have shown
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.
Latent SDEs on Homogeneous Spaces
We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE).
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.