Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Simple, robust, scalable semi-supervised learning via expectation regularization
Proceedings of the 24th international conference on Machine learning
A scalable modular convex solver for regularized risk minimization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating labels from label proportions
Proceedings of the 25th international conference on Machine learning
Supervised Learning by Training on Aggregate Outputs
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Maximum entropy distribution estimation with generalized regularization
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Unifying divergence minimization and statistical inference via convex duality
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Rademacher averages and phase transitions in Glivenko-Cantelli classes
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
Finding deceptive opinion spam by any stretch of the imagination
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
The Journal of Machine Learning Research
Learning from label proportions by optimizing cluster model selection
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Learning naive Bayes models for multiple-instance learning with label proportions
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Unsupervised Supervised Learning II: Margin-Based Classification Without Labels
The Journal of Machine Learning Research
Estimation based on RBM from label proportions in large group case
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Learning Bayesian network classifiers from label proportions
Pattern Recognition
Multi-class learning from class proportions
Neurocomputing
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Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, possibly with known label proportions. This problem occurs in areas like e-commerce, politics, spam filtering and improper content detection. We present consistent estimators which can reconstruct the correct labels with high probability in a uniform convergence sense. Experiments show that our method works well in practice.