Learning Graphical Models
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy that best fits observed sequences of states and actions implemented by an expert.
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.