This tutorial explains why real-world datasets often inherently possess multiple latent structures and demonstrate how deep multiple clustering methods uncover these alternative perspectives, and critically discuss evaluation metrics for clustering quality and diversity.
Bangyu Zou, Jiawei Yao, Hui-Ji Gao et al.· Proceedings of the 32nd ACM...· 0 citations
This tutorial delivers a systematic, data-centric roadmap to build LLM agents that are not merely capable but provably trustworthy, unifying advances in LLM agents, robust machine learning, and data-centric AI.
Tian-Long Chen, Jian Pei, Minxing Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
This paper proposes Concept-guided Counterfactual Subgraph Retrieval (CCSGR), a dataset-grounded formulation that retrieves from a large graph subgraphs that are structurally and semantically similar to a query but induce different predictions under the same model, yielding domain-valid and verifiable counterfactual ex...
Hsi-Wen Chen, Jian Pei, De-Nian Yang et al.· Proceedings of the 32nd ACM...· 0 citations
Large language model (LLM) based agents are evolving from conversational chatbots into autonomous decision-makers that plan, reason, wield tools, and collaborate across high-stakes domains such as healthcare, finance, and scientific discovery. Yet this power brings a fundamental challenge: trustworthiness. How can we g...
Tianlong Chen, Jian Pei, Minxing Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
Counterfactual analysis aims to identify minimal and semantically meaningful changes that alter a model's prediction. Existing perturbation approaches generate counterfactuals by directly editing nodes, edges, or attributes, often producing explanations that are model-dependent and detached from the underlying data dis...
Hsi-Wen Chen, Jian Pei, De-Nian Yang et al.· Proceedings of the 32nd ACM...· 0 citations
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