Multi-Task Learning with Sequential Dependence Toward Industrial Applications: A Systematic Formulation
Abstract
References
Index Terms
- Multi-Task Learning with Sequential Dependence Toward Industrial Applications: A Systematic Formulation
Recommendations
Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsIn online recommendation, financial service, etc., the most common application of multi-task learning (MTL) is the multi-step conversion estimations. A core property of the multi-step conversion is the sequential dependence among tasks. However, most ...
Focused multi-task learning in a Gaussian process framework
Multi-task learning, learning of a set of tasks together, can improve performance in the individual learning tasks. Gaussian process models have been applied to learning a set of tasks on different data sets, by constructing joint priors for functions ...
Focused multi-task learning using gaussian processes
ECML PKDD'11: Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part IIGiven a learning task for a data set, learning it together with related tasks (data sets) can improve performance. Gaussian process models have been applied to such multi-task learning scenarios, based on joint priors for functions underlying the tasks. ...
Comments
Information & Contributors
Information
Published In

Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 365Total Downloads
- Downloads (Last 12 months)288
- Downloads (Last 6 weeks)13
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in