{"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":"/code/mmdu","entry":"MMDu","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":5,"n_papers_ran":5,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":4,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":0,"ran":0,"unverified":0},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2506.05047","paper":"/paper/reliably-detecting-model-failures-in","title":"Reliably detecting model failures in deployment without labels","date":"2025-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fengliu90/DK-for-TST","path":"utils.py","file_url":"https://github.com/fengliu90/DK-for-TST/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b2fd0117a1785dcf","mcp_get_code":{"code_sha256":"b2fd0117a1785dcf"}},{"arxiv_id":"2402.16041","paper":"/paper/detecting-machine-generated-texts-by-multi","title":"Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy","date":"2024-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zshsh98/mmd-mp","path":"utils_MMD.py","file_url":"https://github.com/zshsh98/mmd-mp/blob/HEAD/utils_MMD.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d65d3167162aac86","mcp_get_code":{"code_sha256":"d65d3167162aac86"}},{"arxiv_id":"2306.08777","paper":"/paper/mmd-fuse-learning-and-combining-kernels-for","title":"MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting","date":"2023-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"antoninschrab/mmdfuse-paper","path":"other_tests/deep_mmd_not_image.py","file_url":"https://github.com/antoninschrab/mmdfuse-paper/blob/HEAD/other_tests/deep_mmd_not_image.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b2fd0117a1785dcf","mcp_get_code":{"code_sha256":"b2fd0117a1785dcf"}},{"arxiv_id":"2206.08843","paper":"/paper/automl-two-sample-test","title":"AutoML Two-Sample Test","date":"2022-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jmkuebler/automl-tst-paper","path":"deep_kernel_utils.py","file_url":"https://github.com/jmkuebler/automl-tst-paper/blob/HEAD/deep_kernel_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80d97d29c4bae90a","mcp_get_code":{"code_sha256":"80d97d29c4bae90a"}},{"arxiv_id":"2106.07636","paper":"/paper/meta-two-sample-testing-learning-kernels-for","title":"Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data","date":"2021-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fengliu90/MetaTesting","path":"MetaTST.py","file_url":"https://github.com/fengliu90/MetaTesting/blob/HEAD/MetaTST.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2260b5465b7cf689","mcp_get_code":{"code_sha256":"2260b5465b7cf689"}}]}