{"id":"W3169278660","doi":"10.48550/arxiv.2106.07636","title":"Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Kernel (algebra); Exploit; Machine learning; Sample (material); Task (project management); Artificial intelligence; Multiple kernel learning; Kernel method; Data mining; Mathematics; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01805156,0.001777903,0.002589433,0.002405364,0.0008149026,0.001970881,0.005675621,0.003975697,0.001940636],"category_scores_gemma":[0.1001978,0.0007474041,0.001727702,0.00173295,0.003645559,0.006315256,0.006029997,0.00429154,0.0009969847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083435,"about_ca_system_score_gemma":0.001631834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006528962,"about_ca_topic_score_gemma":0.0007967011,"domain_scores_codex":[0.9894476,0.006518382,0.0005401009,0.001703298,0.001410194,0.000380401],"domain_scores_gemma":[0.9196334,0.05442463,0.004416797,0.01679831,0.002859864,0.001866917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002489091,0.001023873,0.03694877,0.0007588954,0.0008722713,0.0005657419,0.0007782936,0.2791921,0.0173787,0.08173337,0.005987003,0.5722719],"study_design_scores_gemma":[0.00008276152,0.0003680395,0.001559197,0.00004158813,0.00004739726,0.0002513324,0.00005168249,0.9421157,0.005792027,0.0488189,0.0008309222,0.00004038424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03906089,0.0003654285,0.9576291,0.000337566,0.00004754412,0.0001283985,0.0001009028,0.00164267,0.0006874633],"genre_scores_gemma":[0.6595585,0.0001546458,0.3375627,0.000394592,0.0001207664,0.0003865015,0.0005669412,0.000353265,0.000902043],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01805156,"threshold_uncertainty_score":0.09546691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4448532497117624,"score_gpt":0.2457316516869512,"score_spread":0.1991215980248112,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}