Last year version: Winter 2017
Newer version: Winter 2019
Structured prediction is the problem of learning a prediction mapping between inputs and structured outputs, i.e. outputs that are made of interrelated parts often subject to constraints. Examples include predicting trees, orderings, alignments, etc., and appear in many applications from computer vision, natural language processing and computational biology among others.
This is an advanced machine learning course that will focus on the fundamental principles and related tools for structured prediction. The course will review the state of the art, tie older and newer approaches together, as well as identify open questions. It will consist of a mix of faculty lectures, class discussions and paper presentations by students, as well as a research project.
Prerequisite: I will assume that most of the content of IFT 6269 Probabilistic Graphical Models is known by the students.
Teacher: Simon Lacoste-Julien, Office: 3339 André-Aisenstadt
Winter 2018 semester:
NOTE: first lecture is Tuesday January 23rd, 2018
Tuesdays 14h30-17h30 - Y-115 Pav. Roger-Gaudry
Fridays 14h30-16h30 - Y-117 Pav. Roger-Gaudry
Last lecture is Tuesday March 27th; poster session is Friday April 27th 2-5pm.
Spring break: lectures on March 6th and March 9th in AA 1409.
Project - project report to hand in + poster presentation on April 27th | detailed info
energy-based models & surrogate losses
theory of structured prediction: consistency, etc.
the generative learning / discriminative learning continuum
conditional random field (CRF)
structured SVM
latent variable structured SVM, CCCP algorithm
large-scale optimization: Frank-Wolfe, variance-reduced SGD, block-coordinate methods
learning to search
RNN & friends
combinatorial algorithms: min-cost network flow, submodular optimization, dynamic programs