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Jose Ayres Pereira

dblp:74/10432 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 67% Learning theory · 33%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models		 0.112009
Which graphical models are difficult to learn? · NIPS 2009
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › structure learning › graphical model structure learning
ising model structure learning		 0.112009
Which graphical models are difficult to learn? · NIPS 2009

Methods — techniques the papers use, named apart from their topics

statistical learning theory · 0.1phase transition analysis · 0.1
YearPublicationVenuePosition
2009 Which graphical models are difficult to learn?
abstract
We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-complexity algorithms systematically fail when the Markov random field develops long-range correlations. More precisely, this phenomenon appears to be related to the Ising model phase transition (although it does not coincide with it).
Andrea Montanari, Jose Ayres Pereira
 NIPS2