40 citations · 138 across the 12 of their papers we have counts for
12 papers
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision
Jieyu Zhang, Linxin Song, Alexander Ratner
Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently. The core component of PWS is the label model, which infers true…
Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification
Linxin Song, Jieyu Zhang, Tianxiang Yang +1
To obtain a large amount of training labels inexpensively, researchers have recently adopted the weak supervision (WS) paradigm, which leverages labeling rules to synthesize traini…
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh +1
Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end mode…
A Survey on Programmatic Weak Supervision
Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu +2
Labeling training data has become one of the major roadblocks to using machine learning. Among various weak supervision paradigms, programmatic weak supervision (PWS) has achieved…
TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations
Minhao Jiang, Xiangchen Song, Jieyu Zhang +1
Taxonomies are fundamental to many real-world applications in various domains, serving as structural representations of knowledge. To deal with the increasing volume of new concept…
Optimizing Information-theoretical Generalization Bounds via Anisotropic Noise in SGLD
Bohan Wang, Huishuai Zhang, Jieyu Zhang +3
Recently, the information-theoretical framework has been proven to be able to obtain non-vacuous generalization bounds for large models trained by Stochastic Gradient Langevin Dyna…