Graphical Models: Inference and Learning

Graphical Models (Inference and Learning)

Karteek Alahari, Yuliya Tarabalka

Graphical models (or probabilistic graphical models) provide a powerful paradigm to jointly exploit probability theory and graph theory for solving complex real-world problems. They form an indispensable component in several research areas, such as statistics, machine learning, computer vision, where a graph expresses the conditional (probabilistic) dependence among random variables.

This course will focus on discrete models, that is, cases where the random variables of the graphical models are discrete. After an introduction to the basics of graphical models, the course will then focus on problems in representation, inference, and learning of graphical models. We will cover classical as well as state of the art algorithms used for these problems. Several applications in machine learning and computer vision will be studied as part of the course.

All the classes will be held at the new Gif-sur-Yvette campus of CentraleSupelec, in EB.106, Eiffel building.

27/11	Introduction to the course [slides] Graphical Models [slides]
4/12	Belief propagation [slides]
11/12	Graph cuts [slides]
18/12	Primal dual + Dual decomposition [slides] + Paper 1 presentation
8/1	Learning I [slides]
15/1	Bayesian Networks [slides] + Paper 2 presentation
22/1	Learning: CNNs [slides]
12/2	Modern Learning [slides] + Recommender Systems [slides] + Paper 3 presentation
19/2	Exam (3h)

Last year's exam (as a model): 2017

Papers to be presented

1. (18/12) Tarabalka et al., Spatio-temporal video segmentation with shape growth or shrinkage constraint, Trans. Image Processing 2014 (presenters: H. Zenati, O. Sakhi)
2. (15/1) Nowozin et al, On Parameter Learning in CRF-based Approaches to Object Class Image Segmentation, ECCV 2010 (presenters: S. Bouabid, C. Blanco)
3. (12/2) Zheng et al., Conditional Random Fields as Recurrent Neural Networks, ICCV 2015 (presenters: S. Tarmoun, B. Devillers)

Bibliography
Probabilistic graphical models: principles and techniques, Daphne Koller and Nir Friedman, MIT Press
Convex Optimization, Stephen Boyd and Lieven Vanderbeghe
Numerical Optimization, Jorge Nocedal and Stephen J. Wright
Introduction to Operations Research, Frederick S. Hillier and Gerald J. Lieberman
An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs, M. Pawan Kumar, Vladimir Kolmogorov and Phil Torr
Convergent Tree-reweighted Message Passing for Energy Minimization, Vladimir Kolmogorov