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{{short description|Solving multiple machine learning tasks at the same time}}
'''Multi-task learning''' (MTL) is a subfield of [[machine learning]] in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.<ref>Baxter, J. (2000). A model of inductive bias learning" ''Journal of Artificial Intelligence Research'' 12:149--198, [http://www-2.cs.cmu.edu/afs/cs/project/jair/pub/volume12/baxter00a.pdf On-line paper]</ref><ref>[[Sebastian Thrun|Thrun, S.]] (1996). Is learning the n-th thing any easier than learning the first?. In Advances in Neural Information Processing Systems 8, pp. 640--646. MIT Press. [http://citeseer.ist.psu.edu/thrun96is.html Paper at Citeseer]</ref><ref name=":2">{{Cite journal|url = http://www.cs.cornell.edu/~caruana/mlj97.pdf|title = Multi-task learning|last = Caruana|first = R.|date = 1997|journal = Machine Learning|doi = 10.1023/A:1007379606734|volume=28|pages=41–75|doi-access = free}}</ref>
Inherently, Multi-task learning is a [[multi-objective optimization]] problem having [[Trade-off|trade-offs]] between different tasks<ref>Multi-Task Learning as Multi-Objective Optimization Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018), https://proceedings.neurips.cc/paper/2018/hash/432aca3a1e345e339f35a30c8f65edce-Abstract.html</ref>. Early versions of MTL were called "hints".<ref>Suddarth, S., Kergosien, Y. (1990). Rule-injection hints as a means of improving network performance and learning time. EURASIP Workshop. Neural Networks pp. 120-129. Lecture Notes in Computer Science. Springer.</ref><ref>{{cite journal | last1 = Abu-Mostafa | first1 = Y. S. | year = 1990 | title = Learning from hints in neural networks | journal = Journal of Complexity | volume = 6 | issue = 2| pages = 192–198 | doi=10.1016/0885-064x(90)90006-y| doi-access = free }}</ref> In a widely cited 1997 paper, Rich Caruana gave the following characterization:<blockquote>Multitask Learning is an approach to [[inductive transfer]] that improves [[Generalization error|generalization]] by using the ___domain information contained in the training signals of related tasks as an [[inductive bias]]. It does this by learning tasks in parallel while using a shared [[Representation learning|representation]]; what is learned for each task can help other tasks be learned better.<ref name=":2"/></blockquote>
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