Ghassen Jerfel will present his MSE talk on Monday, May 15th, 2017 at 8:15am in CS 302. The members of his committee are Barbara Engelhardt (adviser) and Elad Hazan. The title and abstract of his talk are below. All are welcome to attend. Title: Combining multi-modality and non-linearity for density estimation: Boosted Stochastic Backpropagation for Variational Inference Abstract: Deep Generative Models, such as Variational Autoencoders, couple the structured and regularized latent variable representations of probabilistic graphical models with nonlinear likelihoods to learn flexible representations of complex high-dimensional data. Variational inference provides fast approximation of the posterior for VAEs as it formulates the problem as an optimization problem to find the closest posterior over some parametric family of distributions. However, general VI theory and standard VAE literature restrict the approximation family such that it does not contain the true posterior regardless of how long VI is run (e.g. limited to unimodal diagonal Gaussians for VAEs). In this paper, we propose a model-free, simple to implement, and easily parallelizable meta-algorithm which may be used in conjunction with recent VI advances. Based on the functional gradient descent view of boosting, our algorithm iteratively improves the variational approximation of the posterior and is guaranteed to converge at a rate of O(1/k) to the true posterior. We derive boosted Gaussian stochastic backpropagation as a principled method to incorporate and mix new encoders into a VAE model to refine the estimation of the latent space. We study boosted stochastic backpropagation on standard benchmarks for unsupervised learning, applying it to density estimation tasks on synthetic data, the MNIST, NORB, and OMNIGLOT datasets. We demonstrate competitive results that emphasize the advantage of iterative refinement and multi-modality in DGMs.