40 citations · 52 across the 5 of their papers we have counts for
6 papers
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…
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
Cheng-Yu Hsieh, Wei-I Lin, Miao Xu +3
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where t…
Evaluations and Methods for Explanation through Robustness Analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu +4
Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model. In this paper, we e…
On the (In)fidelity and Sensitivity for Explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala +2
We consider objective evaluation measures of saliency explanations for complex black-box machine learning models. We propose simple robust variants of two notions that have been co…
Automatic Bridge Bidding Using Deep Reinforcement Learning
Chih-Kuan Yeh, Hsuan-Tien Lin
Bridge is among the zero-sum games for which artificial intelligence has not yet outperformed expert human players. The main difficulty lies in the bidding phase of bridge, which r…