477 citations · 1.3k across the 32 of their papers we have counts for
8 papers · 1 filter
Enhancing In-Context Learning via Implicit Demonstration Augmentation
Xiaoling Zhou, Wei Ye, Yidong Wang +4
The emergence of in-context learning (ICL) enables large pre-trained language models (PLMs) to make predictions for unseen inputs without updating parameters. Despite its potential…
A General Framework for Learning from Weak Supervision
Hao Chen, Jindong Wang, Lei Feng +6
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algor…
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
Hao Chen, Ankit Shah, Jindong Wang +6
Learning with reduced labeling standards, such as noisy label, partial label, and multiple label candidates, which we generically refer to as \textit{imprecise} labels, is a common…
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
Hao Chen, Ran Tao, Yue Fan +6
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization pe…
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
Wang Lu, Jindong Wang, Yidong Wang +1
The distribution shifts between training and test data typically undermine the performance of models. In recent years, lots of work pays attention to domain generalization (DG) whe…
USB: A Unified Semi-supervised Learning Benchmark for Classification
Yidong Wang, Hao Chen, Yue Fan +19
Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation pro…
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Yidong Wang, Hao Chen, Qiang Heng +9
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.…
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Bowen Zhang, Yidong Wang, Wenxin Hou +4
The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a pre-d…