**To Join Via Zoom: **To join this seminar, please request Zoom connection details from headsec [at] stat.ubc.ca.

## Presentation 1

**Time:** 11am – 11:30am

**Speaker: **Gian Carlo Di-Luvi, UBC Statistics MSc student

**Title:** Locally-Adaptive Boosting Variational Inference

**Abstract:** Boosting variational inference (BVI) approximates Bayesian posterior densities by iteratively building a mixture of component distributions. However, BVI requires greedily optimizing the next component—an optimization problem that becomes increasingly computationally expensive as more components are added to the mixture. Furthermore, previous work has only used simple (i.e., Gaussian) component distributions; in practice, many of these components are needed to obtain a reasonable approximation. These shortcomings can be addressed by considering components that adapt to the target density. However, natural choices such as MCMC chains do not have tractable densities and thus require a density-free divergence for training.

As a first contribution, we show that the kernelized Stein discrepancy—which to the best of our knowledge is the only density-free divergence feasible for VI—cannot detect when an approximation is missing modes of the target density. Hence, it is not suitable for boosting components with intractable densities. As a second contribution, we develop locally-adaptive boosting variational inference (LBVI), in which each component distribution is a Sequential Monte Carlo (SMC) sampler, i.e., a tempered version of the posterior initialized at a given simple reference distribution. Instead of greedily optimizing the next component, we greedily choose to add components to the mixture and perturb their adaptivity, thereby causing them to locally converge to the target density; this results in refined approximations with considerably fewer components. Moreover, because SMC components have tractable density estimates, LBVI can be used with common divergences (such as the Kullback–Leibler divergence) for model learning. Experiments show that, when compared to previous BVI methods, LBVI produces reliable inference with fewer components and in less computation time.

## Presentation 2

**Time: **11:30am – 12pm

**Speaker:** Kenny Chiu, UBC Statistics MSc student

**Title:** On the Statistical Properties of Entromin as an Orthogonal Rotation Criterion

**Abstract: **The goal in factor analysis is to uncover a set of latent factors that can explain the variation in the data. Principal Component Analysis is one approach that estimates the factors by a set of principal components. However, it may be difficult to interpret the factors as-is, and so it is common to rotate the estimated factors to make their coefficients as sparse as possible to improve interpretability. Varimax is the most popular method for factor rotations, and its statistical properties have been studied in recent literature. Entromin is another factor rotation method that is less commonly used and not as well-studied, but there exists conventional wisdom that Entromin generally finds sparser rotations compared to Varimax.

In this thesis, we aim to explain the sparsity claim for Entromin by studying its statistical properties. Our main contributions include several theoretical results that take steps towards this aim. We show that Varimax is a first-order approximation of Entromin, and that generalizing this connection leads to a family of Entromin approximations. We derive the conditions under which the second-order approximation is expected to identify the true factors in a latent factor model. We then make the connection between optimizing the Entromin objective and recovering sparsity in the factors. Other contributions of this thesis include novel connections to statistical concepts that have not been made in the literature to our knowledge, and an empirical study of Entromin on a real dataset.