{"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/on-structured-prediction-theory-with","title":"On Structured Prediction Theory with Calibrated Convex Surrogate Losses","arxiv_id":"1703.02403","date":"2017-03-07","proceeding":"NeurIPS 2017 12","authors":["Anton Osokin","Francis Bach","Simon Lacoste-Julien"],"abstract":"We provide novel theoretical insights on structured prediction in the context\nof efficient convex surrogate loss minimization with consistency guarantees.\nFor any task loss, we construct a convex surrogate that can be optimized via\nstochastic gradient descent and we prove tight bounds on the so-called\n\"calibration function\" relating the excess surrogate risk to the actual risk.\nIn contrast to prior related work, we carefully monitor the effect of the\nexponential number of classes in the learning guarantees as well as on the\noptimization complexity. As an interesting consequence, we formalize the\nintuition that some task losses make learning harder than others, and that the\nclassical 0-1 loss is ill-suited for general structured prediction.","url_abs":"http://arxiv.org/abs/1703.02403v4","url_pdf":"http://arxiv.org/pdf/1703.02403v4.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":"on-structured-prediction-theory-with","repo_url":"https://github.com/aosokin/consistentSurrogates_derivations","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.02403","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}