{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/asynchronous-multi-task-learning","title":"Asynchronous Multi-Task Learning","arxiv_id":"1609.09563","date":"2016-09-30","proceeding":null,"authors":["Inci M. Baytas","Ming Yan","Anil K. Jain","Jiayu Zhou"],"abstract":"Many real-world machine learning applications involve several learning tasks\nwhich are inter-related. For example, in healthcare domain, we need to learn a\npredictive model of a certain disease for many hospitals. The models for each\nhospital may be different because of the inherent differences in the\ndistributions of the patient populations. However, the models are also closely\nrelated because of the nature of the learning tasks modeling the same disease.\nBy simultaneously learning all the tasks, multi-task learning (MTL) paradigm\nperforms inductive knowledge transfer among tasks to improve the generalization\nperformance. When datasets for the learning tasks are stored at different\nlocations, it may not always be feasible to transfer the data to provide a\ndata-centralized computing environment due to various practical issues such as\nhigh data volume and privacy. In this paper, we propose a principled MTL\nframework for distributed and asynchronous optimization to address the\naforementioned challenges. In our framework, gradient update does not wait for\ncollecting the gradient information from all the tasks. Therefore, the proposed\nmethod is very efficient when the communication delay is too high for some task\nnodes. We show that many regularized MTL formulations can benefit from this\nframework, including the low-rank MTL for shared subspace learning. Empirical\nstudies on both synthetic and real-world datasets demonstrate the efficiency\nand effectiveness of the proposed framework.","url_abs":"http://arxiv.org/abs/1609.09563v1","url_pdf":"http://arxiv.org/pdf/1609.09563v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"asynchronous-multi-task-learning","repo_url":"https://github.com/illidanlab/AMTL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.09563","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}