AI & Fundamentals

Learning-Algorithms from Bayesian Principle - Emtiyaz Khan, Visiting Professor, TUAT, Team Leader at RIKEN Center for AIP

Abstract:

In machine learning, new learning algorithms are designed by borrowing ideas from optimization and statistics followed by an extensive empirical efforts to make them practical. However, there is a lack of underlying principles to guide this process. I will present a stochastic learning algorithm derived from Bayesian principle. Using this algorithm, we can obtain a range of existing algorithms: from classical methods such as least-squares, Newton's method, and Kalman filter to new deep-learning algorithms such as RMSprop and Adam. Surprisingly, using the same principles, new algorithms can be naturally obtained even for the challenging learning tasks such as online learning, continual learning, and reinforcement learning. This talk will summarize recent works and outline future directions on how this principle can be used to make algorithms that mimic the learning behaviour of living beings.

Bio:

Emtiyaz Khan (also known as Emti) is a team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. He is also a visiting professor at the Tokyo University of Agriculture and Technology (TUAT). Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For the past 10 years, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.

Host:

Associate Professor Mark Schmidt, Computer Science, UBC

CAIDA Contact: 

Arynn Keane

arynnk@mail.ubc.ca