{"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/the-importance-of-better-models-in-stochastic","title":"The importance of better models in stochastic optimization","arxiv_id":"1903.08619","date":"2019-03-20","proceeding":null,"authors":["Hilal Asi","John C. Duchi"],"abstract":"Standard stochastic optimization methods are brittle, sensitive to stepsize\nchoices and other algorithmic parameters, and they exhibit instability outside\nof well-behaved families of objectives. To address these challenges, we\ninvestigate models for stochastic minimization and learning problems that\nexhibit better robustness to problem families and algorithmic parameters. With\nappropriately accurate models---which we call the aProx family---stochastic\nmethods can be made stable, provably convergent and asymptotically optimal;\neven modeling that the objective is nonnegative is sufficient for this\nstability. We extend these results beyond convexity to weakly convex\nobjectives, which include compositions of convex losses with smooth functions\ncommon in modern machine learning applications. We highlight the importance of\nrobustness and accurate modeling with a careful experimental evaluation of\nconvergence time and algorithm sensitivity.","url_abs":"http://arxiv.org/abs/1903.08619v1","url_pdf":"http://arxiv.org/pdf/1903.08619v1.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":"the-importance-of-better-models-in-stochastic","repo_url":"https://github.com/HilalAsi/APROX-Robust-Stochastic-Optimization-Algorithms","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"stochastic-optimization","task_name":"Stochastic Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1903.08619","atlas_url":"https://app.syntology.ai/?focus=1903.08619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.08619"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/HilalAsi/APROX-Robust-Stochastic-Optimization-Algorithms","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"7996b17947d822fe","entry":"TimesToEpsilonAccuracy","repo":"HilalAsi/APROX-Robust-Stochastic-Optimization-Algorithms","repo_kind":"listed","path":"paper-code/stability_check.py","file_url":"https://github.com/HilalAsi/APROX-Robust-Stochastic-Optimization-Algorithms/blob/HEAD/paper-code/stability_check.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7996b17947d822fe"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}