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Unfortunately, if there exist multiple distinct but\naccurate models for some dataset, current machine learning methods are unlikely\nto find them: standard techniques will likely recover a complex model that\ncombines them. In this work, we introduce a way to identify a maximal set of\ndistinct but accurate models for a dataset. We demonstrate empirically that, in\nsituations where the data supports multiple accurate classifiers, we tend to\nrecover simpler, more interpretable classifiers rather than more complex ones.","url_abs":"http://arxiv.org/abs/1806.08716v2","url_pdf":"http://arxiv.org/pdf/1806.08716v2.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":"learning-qualitatively-diverse-and","repo_url":"https://github.com/dtak/local-independence-public","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-qualitatively-diverse-and","repo_url":"https://github.com/dtak/lit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1806.08716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.08716"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. 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