The goal is to recover the clusters with minimum number of such queries. This problem has been of recent interest for the case of disjoint clusters.

Experimental data does not suffer from confounding but is usually limited in both scope and scale.

Almost Linearity T argeting, based on medium-scale almost linearity assumptions.

Weconsider the pool-based activelearning problem, where only asubset ofthe training data is labeled, and the goal is to query a batch of unlabeled samples to be labeled so as to maximally improve model performance.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

For labels taking values in a finite metric space, we introduce techniques new to weak supervision based on pseudo-Euclidean embeddings andtensor decompositions, providing anearly-consistent noise rate estimator.