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Multi-task learning for HIV therapy screening
Abstract
We address the problem of learning classifiers for a large number of tasks. We derive a solution that produces resampling weights which match the pool of all examples to the target distribution of any given task. Our work is motivated by the problem of predicting the outcome of a therapy attempt for a patient who carries an HIV virus with a set of observed genetic properties. Such predictions need to be made for hundreds of possible combinations of drugs, some of which use similar biochemical mechanisms. Multi-task learning enables us to make predictions even for drug combinations with few or no training examples and substantially improves the overall prediction accuracy.
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Cited by
A Survey on Multi-Task Learning
Zhang, Y., & Yang, Q. (2017) · arXiv · doi:10.48550/arxiv.1707.08114 · Cited by 2,910
A survey on data‐efficient algorithms in big data era
Adadi, A. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00419-9 · Cited by 331
A Survey of Transfer and Multitask Learning in Bioinformatics
Xu, Q., & Yang, Q. (2011) · Journal of Computing Science and Engineering · doi:10.5626/jcse.2011.5.3.257 · Cited by 89
Metadata
- Authors
- Bickel, S., Bogojeska, J., Lengauer, T., & Scheffer, T.
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 56-63
- OpenAlex
- W2133491790
- Cited by
- 177
- References
- 0
- FWCI
- 11.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Multi-task learning for HIV therapy screening. https://app.cochat.ai/papers/10.1145/1390156.1390164
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- Bickel, S., Bogojeska, J., Lengauer, T., & Scheffer, T. (2008). Multi-task learning for HIV therapy screening. Proceedings of the 25th international conference on Machine learning - ICML '08. https://doi.org/10.1145/1390156.1390164
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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