The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning Gaussian processes from multiple tasks
Abstract
We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (GPs). The resulting models can be learned easily via an EM-algorithm. Empirical studies on multi-label text categorization suggest that the presented models allow accurate solutions of these multi-task problems.
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Cited by
A Survey on Multi-Task Learning
Zhang, Y., & Yang, Q. (2017) · arXiv · doi:10.48550/arxiv.1707.08114 · Cited by 2,910
Jayaram, V., Alamgir, M., Altun, Y., Scholkopf, B., & Grosse-Wentrup, M. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2501545 · Cited by 468
Probabilistic Model-Agnostic Meta-Learning
Finn, C., Xu, K., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1806.02817 · Cited by 327
Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
Joint modeling of heterogeneous sensing data for depression assessment via multi-task learning
Lu, J., Shang, C., Yue, C., Morillo, R., Ware, S., Kamath, J., Bamis, A., Russell, A., Wang, B., & Bi, J. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3191753 · Cited by 85
Metadata
- Authors
- Yu, K., Tresp, V., & Schwaighofer, A.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 1012-1019
- OpenAlex
- W2148522164
- Cited by
- 380
- References
- 1
- FWCI
- 10.49
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yu, K., Tresp, V., & Schwaighofer, A. (2005). Learning Gaussian processes from multiple tasks. Proceedings of the 22nd international conference on Machine learning - ICML '05. https://doi.org/10.1145/1102351.1102479
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
