Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Labeling training data has become one of the major roadblocks to using machine learning.
Among various weak supervision paradigms, programmatic weak supervision (PWS) has …

Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity
on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set …

Algorithmic decision-making systems are successfully being adopted in a wide range of
domains for diverse tasks. While the potential benefits of algorithmic decision-making are …

The abundance of digital documents offers a valuable chance to gain insights into public
opinion, social structure, and dynamics. However, the scale and volume of these digital …

Weak Supervision (WS) techniques allow users to efficiently create large training datasets
by programmatically labeling data with heuristic sources of supervision. While the success of …

Semi-supervised learning has shown promise in allowing NLP models to generalize from
small amounts of labeled data. Meanwhile, pretrained transformer models act as black-box …

Labeling training examples at scale is a perennial challenge in machine learning. Self-
supervision methods compensate for the lack of direct supervision by leveraging prior …

Programmatic weak supervision methodologies facilitate the expedited labeling of extensive
datasets through the use of label functions (LFs) that encapsulate heuristic data sources …