5 citations · 11 across the 5 of their papers we have counts for
6 papers
Weakly Supervised Label Learning Flows
You Lu, Wenzhuo Song, Chidubem Arachie +1
Supervised learning usually requires a large amount of labelled data. However, attaining ground-truth labels is costly for many tasks. Alternatively, weakly supervised methods lear…
Data Consistency for Weakly Supervised Learning
Chidubem Arachie, Bert Huang
In many applications, training machine learning models involves using large amounts of human-annotated data. Obtaining precise labels for the data is expensive. Instead, training w…
Constrained Labeling for Weakly Supervised Learning
Chidubem Arachie, Bert Huang
Curation of large fully supervised datasets has become one of the major roadblocks for machine learning. Weak supervision provides an alternative to supervised learning by training…
Unsupervised Detection of Sub-events in Large Scale Disasters
Chidubem Arachie, Manas Gaur, Sam Anzaroot +3
Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post useful information on what is…
Stochastic Generalized Adversarial Label Learning
Chidubem Arachie, Bert Huang
The usage of machine learning models has grown substantially and is spreading into several application domains. A common need in using machine learning models is collecting the dat…
Adversarial Label Learning
Chidubem Arachie, Bert Huang
We consider the task of training classifiers without labels. We propose a weakly supervised method---adversarial label learning---that trains classifiers to perform well against an…