Learning Graphical Models
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts.
A Statistical Online Inference Approach in Averaged Stochastic Approximation
FCL T results in the Markov chain setting, an alternative condition that is more applicable for SA problems is established.
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
We study pointwise estimation and uncertainty quantification for a sparse varia-tional Gaussian process method with eigenvector inducing variables.
FlowHMM: Flow-based continuous hidden Markov models
Continuous hidden Markov models (HMMs) assume that observations are generated from a mixture of Gaussian densities, limiting their ability to model more complex distributions.
FedPop: A Bayesian Approach for Personalised Federated Learning
Personalised federated learning (FL) aims at collaboratively learning a machine learning model tailored for each client.
Tractable Latent State Inference for Hidden Continuous-Time semi-Markov Chains Supplement
We will first replicate an equation similar to (20) for the backward case. The derivation is similar to that of the forward equation, so that it uses a combination of equations (16), (18) and (19) while leaving out the observation likelihood function. The combination is again carried out using the Laplace transform.