5 papers
MAADBench: The Refreshable Paradigm for Anomaly Detection in Multi-Agent Systems
Lei Ma, Dennis Hofmann, Haowen Xu +4
Recent studies report that LLM-based multi-agent systems (MAS) fail at rates of 41%-87%, yet to our knowledge, no benchmark to date supports systematic anomaly detection (AD) for t…
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
Ruofan Hu, Dongyu Zhang, Huayi Zhang +1
Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labele…
Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics
Oluseun Olulana, Kathleen Cachel, Fabricio Murai +1
As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (L…
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance
Dongyu Zhang, Ruofan Hu, Elke Rundensteiner
Deep neural networks (DNNs) have advanced many machine learning tasks, but their performance is often harmed by noisy labels in real-world data. Addressing this, we introduce CoLaf…
UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection
Ruofan Hu, Dongyu Zhang, Dandan Tao +3
Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodb…