{"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/position-adopt-constraints-over-penalties-in","title":"Position: Adopt Constraints Over Penalties in Deep Learning","arxiv_id":"2505.20628","date":"2025-05-27","proceeding":null,"authors":["Juan Ramirez","Meraj Hashemizadeh","Simon Lacoste-Julien"],"abstract":"Recent efforts toward developing trustworthy AI systems with accountability guarantees have led to a growing reliance on machine learning formulations that incorporate external requirements, or constraints. These requirements are often enforced through penalization--adding fixed-weight terms to the task loss. We argue that this approach is ill-suited, and that tailored constrained optimization methods should be adopted instead. In particular, no penalty coefficient may yield a solution that both satisfies the constraints and achieves good performance--i.e., one solving the constrained problem. Moreover, tuning these coefficients is costly, incurring significant time and computational overhead. In contrast, tailored constrained methods--such as the Lagrangian approach, which optimizes the penalization \"coefficients\" (the Lagrange multipliers) alongside the model--(i) truly solve the constrained problem and add accountability, (ii) eliminate the need for extensive penalty tuning, and (iii) integrate seamlessly with modern deep learning pipelines.","url_abs":"https://arxiv.org/abs/2505.20628v1","url_pdf":"https://arxiv.org/pdf/2505.20628v1.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":"position-adopt-constraints-over-penalties-in","repo_url":"https://github.com/merajhashemi/constraints-vs-penalties","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":null,"task_name":"Position"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2505.20628","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}