Seminar: Approximate Bayesian Inference: Bringing Statistics, Optimization, and Machine Learning Together.

SpeakerEmtiyaz Khan
AffiliationEPFL
DateFriday, 18 Mar 2016
Time13:00 - 14:00
LocationRoberts G08 Sir David Davies LT (TBC)
Event seriesDeepMind CSML Seminar Series
Description

Abstract:
Machine learning relies heavily on data to design computers that can learn autonomously, but dealing with noisy, unreliable, heterogeneous, high-dimensional, and missing data is a big challenge in itself. Surprisingly, living beings - even young ones - are very good in dealing with such data. This raises the question: how do they do it, and how can we design computers that can learn like them?

Bayesian methods are promising in answering such questions, but they are computationally challenging, especially when data are large and models are complex. In this talk, I will start by showing a few example applications where this is the case. I will then discuss my work which solves many computational challenges associated with Bayesian methods by converting the "Bayesian integration" problem into an optimization problem. I will outline some of my future plans to design linear-time algorithms for Bayesian inference. Overall, I will argue that, by combining ideas from statistics, optimization, and machine learning, we might be able to design computers that can learn autonomously, just like us.

A short biography:
Mohammad Emtiyaz Khan is a scientist in the School of Computer and Communication Sciences at the École polytechnique fédérale de Lausanne (EPFL). He obtained his PhD from the University of British Columbia (UBC) in 2012 under the supervision of Dr. Kevin Murphy, and later he worked as a post-doctoral fellow at EPFL under Dr. Matthias Seeger. His research lies at the intersection of machine learning, statistics, and optimization, and their applications to a wide-variety of areas such as health, education, sensor networks, social networks, and biomedicine. He has been actively involved in teaching, especially large courses in machine learning, for which he has received several teaching awards and prizes.

Speaker website

iCalendar csml_id_261.ics