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    ICML
    2009
    IEEE
    150views Machine Learning» more  ICML 2009»
    Boosting with structural sparsity
    14 years 5 months ago
    Boosting with structural sparsity
    Download  www.cs.berkeley.edu
    Despite popular belief, boosting algorithms and related coordinate descent methods are prone to overfitting. We derive modifications to AdaBoost and related gradient-based coordin...
    John Duchi, Yoram Singer
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    ICML
    2009
    IEEE
    163views Machine Learning» more  ICML 2009»
    Structure learning of Bayesian networks using constraints
    14 years 5 months ago
    Structure learning of Bayesian networks using constraints
    Download  www.ecse.rpi.edu
    This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
    Cassio Polpo de Campos, Zhi Zeng, Qiang Ji
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    ICML
    2009
    IEEE
    137views Machine Learning» more  ICML 2009»
    Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery
    14 years 5 months ago
    Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery
    Download  www.stat.purdue.edu
    We develop a cyclical blockwise coordinate descent algorithm for the multi-task Lasso that efficiently solves problems with thousands of features and tasks. The main result shows ...
    Han Liu, Mark Palatucci, Jian Zhang
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    ICML
    2009
    IEEE
    142views Machine Learning» more  ICML 2009»
    Curriculum learning
    14 years 5 months ago
    Curriculum learning
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    Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more ...
    Jérôme Louradour, Jason Weston, Ronan...
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    ICML
    2009
    IEEE
    160views Machine Learning» more  ICML 2009»
    The Bayesian group-Lasso for analyzing contingency tables
    14 years 5 months ago
    The Bayesian group-Lasso for analyzing contingency tables
    Download  www.machinelearning.org
    Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Ba...
    Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edga...
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    ICML
    2009
    IEEE
    111views Machine Learning» more  ICML 2009»
    The graphlet spectrum
    14 years 5 months ago
    The graphlet spectrum
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    Risi Imre Kondor, Nino Shervashidze, Karsten M. Bo...
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    ICML
    2009
    IEEE
    124views Machine Learning» more  ICML 2009»
    Large-scale deep unsupervised learning using graphics processors
    14 years 5 months ago
    Large-scale deep unsupervised learning using graphics processors
    Download  ai.stanford.edu
    The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
    Rajat Raina, Anand Madhavan, Andrew Y. Ng
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    ICML
    2009
    IEEE
    96views Machine Learning» more  ICML 2009»
    Large-scale collaborative prediction using a nonparametric random effects model
    14 years 5 months ago
    Large-scale collaborative prediction using a nonparametric random effects model
    Download  www.dbs.informatik.uni-muenchen.de
    Kai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gon...
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    ICML
    2009
    IEEE
    188views Machine Learning» more  ICML 2009»
    Convex variational Bayesian inference for large scale generalized linear models
    14 years 5 months ago
    Convex variational Bayesian inference for large scale generalized linear models
    Download  www.kyb.tuebingen.mpg.de
    We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. ...
    Hannes Nickisch, Matthias W. Seeger
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    ICML
    2009
    IEEE
    155views Machine Learning» more  ICML 2009»
    Near-Bayesian exploration in polynomial time
    14 years 5 months ago
    Near-Bayesian exploration in polynomial time
    Download  ai.stanford.edu
    We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to model-based RL offers an elegant solution to this problem, by considering...
    J. Zico Kolter, Andrew Y. Ng
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