6 papers
Learnability with Partial Labels and Adaptive Nearest Neighbors
Nicolas A. Errandonea, Santiago Mazuelas, Jose A. Lozano +1
Prior work on partial labels learning (PLL) has shown that learning is possible even when each instance is associated with a bag of labels, rather than a single accurate but costly…
Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities
Verónica Álvarez, Santiago Mazuelas, Steven An +1
The accurate labeling of datasets is often both costly and time-consuming. Given an unlabeled dataset, programmatic weak supervision obtains probabilistic predictions for the label…
Learning Smooth Distance Functions via Queries
Akash Kumar, Sanjoy Dasgupta
In this work, we investigate the problem of learning distance functions within the query-based learning framework, where a learner is able to pose triplet queries of the form: ``Is…
Online Consistency of the Nearest Neighbor Rule
Sanjoy Dasgupta, Geelon So
In the realizable online setting, a learner is tasked with making predictions for a stream of instances, where the correct answer is revealed after each prediction. A learning rule…
Convergence Behavior of an Adversarial Weak Supervision Method
Steven An, Sanjoy Dasgupta
Labeling data via rules-of-thumb and minimal label supervision is central to Weak Supervision, a paradigm subsuming subareas of machine learning such as crowdsourced learning and s…
New bounds on the cohesion of complete-link and other linkage methods for agglomeration clustering
Sanjoy Dasgupta, Eduardo Laber
Linkage methods are among the most popular algorithms for hierarchical clustering. Despite their relevance the current knowledge regarding the quality of the clustering produced by…