Boosting Multitask Learning on Graphs through Higher-Order Task Affinities-Reference-Cited by-同舟云学术

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Boosting Multitask Learning on Graphs through Higher-Order Task Affinities

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Li Dongyue¹^ORCID,Ju Haotian¹^ORCID,Sharma Aneesh²^ORCID,Zhang Hongyang R.¹^ORCID

Affiliation:

1. Northeastern University, Boston, MA, USA

2. Google, Mountain View, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599265

Reference66 articles.

1. E. Abbe. "Community detection and stochastic block models: recent developments". In: Journal of Machine Learning Research (2017).

2. Aggregating inconsistent information

3. R. Andersen and K. J. Lang. "Communities from seed sets". In: World Wide Web. 2006.

4. Saliency-Regularized Deep Multi-Task Learning

5. S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan. "A theory of learning from different domains". In: Machine learning (2010).

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

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