Rademacher and gaussian complexities: risk bounds and structural results
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
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
Learning from measurements in exponential families
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Hilbert space embeddings of conditional distributions with applications to dynamical systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Attacks on privacy and deFinetti's theorem
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Surrogate learning: from feature independence to semi-supervised classification
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Using weak supervision in learning Gaussian mixture models
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Estimating Labels from Label Proportions
The Journal of Machine Learning Research
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Unsupervised transfer classification: application to text categorization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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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, also with known label proportions. This problem appears in areas like e-commerce, 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.