Monday h 10:30 about probabilistic graphical models
by Announcements of talks＠IDSIA
I recall you that we will have two talks this morning.
First talk will be at 10:30, second talk will start at about 11.
They will be both in room 222.
h10:30
==
Supervised Belief Propagation: Supervised Inference on Complicated Graphs
Mr. Jaemin Yoo, PhD student at the Seoul National University
Abstract:
Belief Propagation (BP) is a general graph inference algorithm for
probabilistic graphical models. Because of its scalability and
generality, BP has been widely used for solving real-world problems:
malware detection, web classification, recommendation, etc.
However, BP has a problem in modelling complicated structures because
the most important parameter in the model, the propagation parameter,
should be chosen manually. Although cross-validation nearly solves the
problem for small models, BP couldn’t enlarge the domain of the
algorithm because of this limitation.
In this work, we propose Supervised Belief Propagation (SBP), a powerful
inference algorithm which trains the propagation parameter using a
gradient-based approach. As a result, it can model much complicated
structures compared to BP including layered graphs, directed graphs, and
featured graphs; none of these could be easily handled by BP. After
that, we show SBP performs better than BP for all the existing problems,
especially as models become complicated. Finally, we show applications
of SBP for the sign prediction and node classification tasks.
==h 11:00
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
Mauro Scanagatta, PhD student at IDSIA.
We present a method for learning treewidth-bounded Bayesian networks from
data sets containing thousands of variables. Bounding the treewidth of a
Bayesian
network greatly reduces the complexity of inferences. Yet, being a
global property
of the graph, it considerably increases the difficulty of the learning
process. Our
novel algorithm scales both to large domains and to large treewidths,
consistently outperforming the state of the art on experiments with up
to ten thousands variables.
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7 years, 9 months
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Monday h 10:30 about probabilistic graphical models
by Announcements of talks＠IDSIA
Hi all,
Hi all,
next week two researchers from Seoul National University will visit us.
We will have two talks to illustrate each other research activities.
First talk will be at 10:30, second talk will start at about 11.
They will be both in room 222.
h10:30
==
Supervised Belief Propagation: Supervised Inference on Complicated Graphs
Mr. Jaemin Yoo, PhD student at the Seoul National University
Abstract:
Belief Propagation (BP) is a general graph inference algorithm for
probabilistic graphical models. Because of its scalability and
generality, BP has been widely used for solving real-world problems:
malware detection, web classification, recommendation, etc.
However, BP has a problem in modelling complicated structures because
the most important parameter in the model, the propagation parameter,
should be chosen manually. Although cross-validation nearly solves the
problem for small models, BP couldn’t enlarge the domain of the
algorithm because of this limitation.
In this work, we propose Supervised Belief Propagation (SBP), a powerful
inference algorithm which trains the propagation parameter using a
gradient-based approach. As a result, it can model much complicated
structures compared to BP including layered graphs, directed graphs, and
featured graphs; none of these could be easily handled by BP. After
that, we show SBP performs better than BP for all the existing problems,
especially as models become complicated. Finally, we show applications
of SBP for the sign prediction and node classification tasks.
==h 11:00
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
Mauro Scanagatta, PhD student at IDSIA.
We present a method for learning treewidth-bounded Bayesian networks from
data sets containing thousands of variables. Bounding the treewidth of a
Bayesian
network greatly reduces the complexity of inferences. Yet, being a
global property
of the graph, it considerably increases the difficulty of the learning
process. Our
novel algorithm scales both to large domains and to large treewidths,
consistently outperforming the state of the art on experiments with up
to ten thousands variables.
7 years, 9 months
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