Search Results for author:
Found 7 papers, 2 papers with code
Weakly Supervised Label Learning Flows
In this paper, we develop label learning flows (LLF), a general framework for weakly supervised learning problems.
Firebolt: Weak Supervision Under Weaker Assumptions
In particular, Firebolt learns the class balance and class-specific accuracy of LFs jointly from unlabeled data.
Data Consistency for Weakly Supervised Learning
Instead, we use weak signals and the data features to solve a constrained optimization that enforces data consistency among the labels we generate.
Constrained Labeling for Weakly Supervised Learning
Curation of large fully supervised datasets has become one of the major roadblocks for machine learning.
Unsupervised Detection of Sub-events in Large Scale Disasters
Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable.
Stochastic Generalized Adversarial Label Learning
In this paper, we propose stochastic generalized adversarial label learning (Stoch-GALL), a framework for training machine learning models that perform well when noisy and possibly correlated labels are provided.
Adversarial Label Learning
We propose a weakly supervised method---adversarial label learning---that trains classifiers to perform well against an adversary that chooses labels for training data.
