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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Yuanchao Dai

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Conference Open access Sep 2026

Label Confidence Recovery with High-order Label Correlation in Partial Multi-label Learning

REHC-PML (Label Confidence REcovery with High-order Label Correlations in Partial Multi-label Learning), which mines frequent high-order co-occurrences from candidate sets to identify global high-order label correlations, selects instance-relevant correlations via Gumbel-Softmax pruning, and propagates their evidence t...

Yuanchao Dai, Xi-Ming Li, Ming-Kun Xie et al. · 0 citations
Open access Aug 2026

Weakly-Supervised Learning with Partial Multi-Labels by Leveraging Dual Label Correlation Perspectives

A novel PML method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (Wpml3cp), solved by the gradient descent with an augmented Lagrange multiplier technique, and empirical results demonstrate that Wpml3cp and Wpml3cp-D can outperform the PML baselines in various noisy levels.

Xi-Ming Li, Yuanchao Dai, Bing Wang et al. · 0 citations
2025

A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study

A comprehensive study to investigate the basic characteristics of current Positive-Unlabeled learning methods and proposes a general framework of PU learning by integrating the set-aware empirical risk with pseudo-labeling.

Yuanchao Dai, Zhengzhang Hou, C. Li et al. · 2 citations

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