Thu, Nov 30, 2017 @ 02:00 PM - 03:20 PM
Conferences, Lectures, & Seminars
Speaker: Andrew Miller, Harvard
Talk Title: Advances in Monte Carlo Variational Inference
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Probabilistic modeling is a natural framework for reasoning about noisy data. Well-constructed probabilistic models that combine prior knowledge with data can uncover latent structure, make predictions, and support scientific discovery. However, specifying a model and actually applying a model to data are two distinct challenges. In this talk, I will illustrate and address these challenges by presenting new models and inference methods. Monte Carlo variational inference (MCVI) is an optimization-based class of approximate inference algorithms applicable to a wide range of probabilistic models. I will present work that improves MCVI by increasing the expressiveness of approximations and the robustness of optimization. I will also present new probabilistic models developed for a variety of applied problems.
Biography: Andy Miller is a PhD candidate in computer science at Harvard University, studying statistical machine learning. He develops probabilistic models and inference methods for complex, high-dimensional data in applications ranging from astronomy to health care to sports analytics. He is currently in the final year of his program, advised by Ryan Adams (Princeton and Google Brain), Finale Doshi-Velez (Harvard), and Luke Bornn (Simon Fraser).
Host: Fei Sha
Location: Seeley G. Mudd Building (SGM) - 123
Audiences: Everyone Is Invited
Contact: Computer Science Department