{"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/hsic","entry":"HSIC","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":5,"n_samples_ran":5,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":2,"ran_fixture":1,"ran":1,"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":"2406.03258","paper":"/paper/relaxed-quantile-regression-prediction","title":"Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tpouplin/rqr","path":"src/RQR-O/losses.py","file_url":"https://github.com/tpouplin/rqr/blob/HEAD/src/RQR-O/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"179e3979e3baf728","mcp_get_code":{"code_sha256":"179e3979e3baf728"}},{"arxiv_id":"2312.10102","paper":"/paper/robust-estimation-of-causal-heteroscedastic","title":"Robust Estimation of Causal Heteroscedastic Noise Models","date":"2023-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"quangdzuytran/ROCHE","path":"causa/hsic_torch.py","file_url":"https://github.com/quangdzuytran/ROCHE/blob/HEAD/causa/hsic_torch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"97c4e23c2181e7fd","mcp_get_code":{"code_sha256":"97c4e23c2181e7fd"}},{"arxiv_id":"2308.11158","paper":"/paper/domain-generalization-via-rationale","title":"Domain Generalization via Rationale Invariance","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liangchen527/RIDG","path":"domainbed/hsic.py","file_url":"https://github.com/liangchen527/RIDG/blob/HEAD/domainbed/hsic.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6079cbcdac151d5","mcp_get_code":{"code_sha256":"b6079cbcdac151d5"}},{"arxiv_id":"2202.01864","paper":"/paper/exploiting-independent-instruments","title":"Exploiting Independent Instruments: Identification and Distribution Generalization","date":"2022-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sorawitj/hsic-x","path":"models/hsicx.py","file_url":"https://github.com/sorawitj/hsic-x/blob/HEAD/models/hsicx.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8f248829afe8d32d","mcp_get_code":{"code_sha256":"8f248829afe8d32d"}},{"arxiv_id":"1908.01580","paper":"/paper/the-hsic-bottleneck-deep-learning-without","title":"The HSIC Bottleneck: Deep Learning without Back-Propagation","date":"2019-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gusye1234/Pytorch-HSIC-bottleneck","path":"torch_hsic.py","file_url":"https://github.com/gusye1234/Pytorch-HSIC-bottleneck/blob/HEAD/torch_hsic.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76846d9d673d577b","mcp_get_code":{"code_sha256":"76846d9d673d577b"}}]}