{"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/multi-task-learning-with-user-preferences","title":"Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization","arxiv_id":null,"date":"2020-01-01","proceeding":"ICML 2020 1","authors":["Debabrata Mahapatra","Vaibhav Rajan"],"abstract":"Multi-Task Learning (MTL) is a well established learning paradigm for jointly learning models for multiple correlated tasks. Often the tasks conflict requiring trade-offs between them during optimization. Recent advances in multi-objective optimization based MTL  have enabled us to use large-scale deep networks to find one or more Pareto optimal solutions. However, they cannot be used to find exact Pareto optimal solutions satisfying user-specified preferences with respect to task-specific losses, that is not only a common requirement in applications but also a useful way to explore the infinite set of Pareto optimal solutions. We develop the first gradient-based multi-objective MTL algorithm to address this problem. Our unique approach combines multiple gradient descent with carefully controlled ascent, that enables it to trace the Pareto front in a principled manner and makes it robust to initialization. Assuming only differentiability of the task-specific loss functions, we provide theoretical guarantees for convergence. We empirically demonstrate the superiority of our algorithm over state-of-the-art methods.","url_abs":"https://proceedings.icml.cc/static/paper_files/icml/2020/3635-Paper.pdf","url_pdf":"https://proceedings.icml.cc/static/paper_files/icml/2020/3635-Paper.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":"multi-task-learning-with-user-preferences","repo_url":"https://github.com/dbmptr/EPOSearch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}