3 citations · 3 across the 15 of their papers we have counts for
16 papers
Avoiding Structural Failure Modes in Tabular Fair SSL: Online Primal-Dual Allocation under Confidence Gating
Hangchuan Liang, Changchun Li
Semi-supervised learning (SSL) enables prediction with limited labels, but high-stakes tabular applications (medical, credit, recidivism) require statistical fairness guarantees. W…
Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection
Bing Wang, Rui Miao, Ximing Li +6
The rapid spread of misinformation on social media platforms has become a formidable challenge. To mitigate its proliferation, Misinformation Detection (MD) has emerged as a critic…
Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning
Bing Wang, Ximing Li, Changchun Li +3
Recently, the prominent performance of large language models (LLMs) has been largely driven by multi-task instruct-tuning. Unfortunately, this training paradigm suffers from a key…
Generalizing Dynamics Modeling More Easily from Representation Perspective
Yiming Wang, Zhengnan Zhang, Genghe Zhang +7
Learning system dynamics from observations is a critical problem in many applications over various real-world complex systems, e.g., climate, ecology, and fluid systems. Recently,…
Learning from Label Proportions with Dual-proportion Constraints
Tianhao Ma, Ximing Li, Changchun Li +1
Learning from Label Proportions (LLP) is a weakly supervised problem in which the training data comprise bags, that is, groups of instances, each annotated only with bag-level clas…
Semi-Supervised Learning with Balanced Deep Representation Distributions
Changchun Li, Ximing Li, Bingjie Zhang +2
Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternativel…
Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios
Bing Wang, Ximing Li, Changchun Li +4
Nowadays, the widespread dissemination of misinformation across numerous social media platforms has led to severe negative effects on society. To address this challenge, the automa…
Enhancing Multimodal Misinformation Detection by Replaying the Whole Story from Image Modality Perspective
Bing Wang, Ximing Li, Yanjun Wang +4
Multimodal Misinformation Detection (MMD) refers to the task of detecting social media posts involving misinformation, where the post often contains text and image modalities. Howe…
Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations
Bing Wang, Ximing Li, Yiming Wang +4
The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effec…
Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors
Bing Wang, Ximing Li, Mengzhe Ye +4
Nowadays, misinformation articles, especially multimodal ones, are widely spread on social media platforms and cause serious negative effects. To control their propagation, Multimo…
Robust Misinformation Detection by Visiting Potential Commonsense Conflict
Bing Wang, Ximing Li, Changchun Li +4
The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Mis…
Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator
Bing Wang, Bingrui Zhao, Ximing Li +3
Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor…
Why Misinformation is Created? Detecting them by Integrating Intent Features
Bing Wang, Ximing Li, Changchun Li +3
Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full…
Harmfully Manipulated Images Matter in Multimodal Misinformation Detection
Bing Wang, Shengsheng Wang, Changchun Li +2
Nowadays, misinformation is widely spreading over various social media platforms and causes extremely negative impacts on society. To combat this issue, automatically identifying m…
Learning with Partial Labels from Semi-supervised Perspective
Ximing Li, Yuanzhi Jiang, Changchun Li +2
Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but…
Topic representation: finding more representative words in topic models
Jinjin Chi, Jihong Ouyang, Changchun Li +3
The top word list, i.e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models. Most of recent automatical topic…