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ICML
2009
IEEE
15 years 27 days ago
Dynamic mixed membership blockmodel for evolving networks
In a dynamic social or biological environment, interactions between the underlying actors can undergo large and systematic changes. Each actor can assume multiple roles and their ...
Wenjie Fu, Le Song, Eric P. Xing
ICML
2009
IEEE
15 years 27 days ago
PAC-Bayesian learning of linear classifiers
We present a general PAC-Bayes theorem from which all known PAC-Bayes risk bounds are obtained as particular cases. We also propose different learning algorithms for finding linea...
Alexandre Lacasse, François Laviolette, Mar...
ICML
2009
IEEE
15 years 27 days ago
Unsupervised search-based structured prediction
We describe an adaptation and application of a search-based structured prediction algorithm "Searn" to unsupervised learning problems. We show that it is possible to red...
Hal Daumé III
ICML
2009
IEEE
15 years 27 days ago
Learning with structured sparsity
This paper investigates a new learning formulation called structured sparsity, which is a naturalextensionofthestandardsparsityconceptinstatisticallearningandcompressivesensing. B...
Junzhou Huang, Tong Zhang, Dimitris N. Metaxas
ICML
2009
IEEE
15 years 27 days ago
Boosting products of base classifiers
Balázs Kégl, Róbert Busa-Feke...
ICML
2009
IEEE
15 years 27 days ago
Bandit-based optimization on graphs with application to library performance tuning
The problem of choosing fast implementations for a class of recursive algorithms such as the fast Fourier transforms can be formulated as an optimization problem over the language...
Arpad Rimmel, Frédéric de Mesmay, Ma...
ICML
2009
IEEE
15 years 27 days ago
Learning dictionaries of stable autoregressive models for audio scene analysis
In this paper, we explore an application of basis pursuit to audio scene analysis. The goal of our work is to detect when certain sounds are present in a mixed audio signal. We fo...
Youngmin Cho, Lawrence K. Saul
ICML
2009
IEEE
15 years 27 days ago
Accounting for burstiness in topic models
Gabriel Doyle, Charles Elkan
ICML
2009
IEEE
15 years 27 days ago
On primal and dual sparsity of Markov networks
Sparsity is a desirable property in high dimensional learning. The 1-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity. Combining the...
Jun Zhu, Eric P. Xing