6 accepted papers
We study the estimation of Zipfian distributions under $L_1$ loss, and provide near minimax optimal bounds in several regimes. Specifically, we assume observations arrive from a known alphabet, and with a known decay rate parametrizing the Zipfian, but we do not know a priori which alphabet element…
We consider the problem of clustering grouped data for which the observations may include group-specific variables in addition to the variables that are shared across groups. This type of data is quite common; for example, in cancer genomic studies, molecular information is available for all cancers…
Variational inference is routinely deployed in Bayesian state-space models as an efficient computational technique. Motivated by the inconsistency issue observed by Wang and Titterington (2004) for the mean-field approximation in linear state-space models, we consider a more expressive variational f…
Computing the marginal likelihood or evidence is one of the core challenges in Bayesian analysis. While there are many established methods for estimating this quantity, they predominantly rely on using a large number of posterior samples obtained from a Markov Chain Monte Carlo (MCMC) algorithm. As…
Transformation based methods have been an attractive approach in non-parametric inference for problems such as unconditioned and conditional density estimation due to their unique hierarchical structure that models the data as flexible transformation of a set of common latent variables. More recentl…
The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation. We provide general conditions for obtaining optimal risk bounds for point estimates acquired from mean-field variational Bayesian inference. The conditions pertain t…