Our second DIRO talk for next week will be Guillaume Rabusseau, currently a postdoc at McGill, giving a talk on Tuesday Feb 27 at 11:00AM in room Claire-McNicoll Z317

Structure your schedules around this talk, it should be intense!
Michael

TITLE Machine Learning with Tensors for Structured Data

KEYWORDS Structured/Graph Learning, Weighted Automata, Multilinear Algebra, ML Theory

ABSTRACT
Over the last few years, the machine learning community has shown a
growing interest towards tensors. The most obvious connection between
tensors and machine learning appears when the data has a natural
tensor structure (e.g. videos, hyperspectral images...) but tensors
can also arise as model parameters or as powerful tools to design
efficient and consistent learning algorithms. In this talk, I will
show how tensor methods can be leveraged for learning with structured
data. I will first consider the case of tensor structured data and
show how taking the tensor structure into account, rather than
vectorizing the data before feeding it to the learning algorithm, can
lead to better computational and generalization performances. The
second part of the talk will be focused on learning with discrete
structured data such as sequences, trees and graphs. I will first
argue that classical computational models such as weighted automata
are particularly suited to represent functions defined over syntactic
objects in the context of machine learning. After outlining the
fundamental connections between weighted automata, tensors, and more
classical learning models such as recurrent neural networks, I will
present recent contributions showcasing the relevance of weighted
automata for learning with structured data. To conclude the talk, I
will give an overall perspective on how multilinear algebra and tensor
methods can be beneficially used in machine learning by presenting
future research directions.

BIO
Guillaume Rabusseau is an IVADO postdoctoral research fellow in the
Reasoning and Learning Lab at McGill University, where he works with
Prakash Panangaden, Joelle Pineau and Doina Precup. His research
interests lie at the intersection of theoretical computer science and
machine learning. He obtained his PhD in computer science in 2016 at
Aix-Marseille University under the supervision of François Denis and
Hachem Kadri. His work revolves around exploring inter-connections
between tensors and machine learning and developing efficient learning
methods for structured data relying on linear and multilinear algebra.