{"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-two-dimensions-of-worst-case-training-and","title":"The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization","arxiv_id":"2204.04384","date":"2022-04-09","proceeding":null,"authors":["Zeyi Huang","Haohan Wang","Dong Huang","Yong Jae Lee","Eric P. Xing"],"abstract":"Training with an emphasis on \"hard-to-learn\" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e.g., generalization across distributions) is valued. Existing literature discussing this \"hard-to-learn\" concept are mainly expanded either along the dimension of the samples or the dimension of the features. In this paper, we aim to introduce a simple view merging these two dimensions, leading to a new, simple yet effective, heuristic to train machine learning models by emphasizing the worst-cases on both the sample and the feature dimensions. We name our method W2D following the concept of \"Worst-case along Two Dimensions\". We validate the idea and demonstrate its empirical strength over standard benchmarks.","url_abs":"https://arxiv.org/abs/2204.04384v1","url_pdf":"https://arxiv.org/pdf/2204.04384v1.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-two-dimensions-of-worst-case-training-and","repo_url":"https://github.com/oodbag/w2d","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2204.04384","atlas_url":"https://app.syntology.ai/?focus=2204.04384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.04384"}},"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/oodbag/w2d","reach":null}],"summary":{"ran":1,"ran_fixture":1,"unverified":2},"by_repo_kind":{"official":{"samples":4,"ran":2,"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":4,"samples":[{"code_sha256_prefix":"f4f62760b15a2f01","entry":"Algorithm","repo":"oodbag/w2d","repo_kind":"official","path":"DomainBed/domainbed/algorithms.py","file_url":"https://github.com/oodbag/w2d/blob/HEAD/DomainBed/domainbed/algorithms.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f4f62760b15a2f01"}},{"code_sha256_prefix":"89948fba894ae3df","entry":"_get_optimizer","repo":"oodbag/w2d","repo_kind":"official","path":"DomainBed/domainbed/algorithms.py","file_url":"https://github.com/oodbag/w2d/blob/HEAD/DomainBed/domainbed/algorithms.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"89948fba894ae3df"}},{"code_sha256_prefix":"ef92685dbb4a6185","entry":"ERM","repo":"oodbag/w2d","repo_kind":"official","path":"DomainBed/domainbed/algorithms.py","file_url":"https://github.com/oodbag/w2d/blob/HEAD/DomainBed/domainbed/algorithms.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":"ef92685dbb4a6185"}},{"code_sha256_prefix":"6570a11274fe2580","entry":"W2D","repo":"oodbag/w2d","repo_kind":"official","path":"DomainBed/domainbed/algorithms.py","file_url":"https://github.com/oodbag/w2d/blob/HEAD/DomainBed/domainbed/algorithms.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":"6570a11274fe2580"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}