{"id":"W2913737986","doi":"10.1109/lra.2019.2897342","title":"Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preference learning; Equivalence (formal languages); Robustness (evolution); Robot; Intuition; Bayesian probability; Bayesian inference; Multi-task learning; Ranking (information retrieval)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008689225,0.0001198921,0.0001302912,0.0001122883,0.0001452998,0.00008406456,0.00004924785,0.0000544605,0.00001375644],"category_scores_gemma":[0.00001838636,0.0001344697,0.0000313716,0.0001940181,0.00002145416,0.000283664,0.00000607011,0.0001277547,0.00001567377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007832119,"about_ca_system_score_gemma":0.00001145052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002027699,"about_ca_topic_score_gemma":9.062913e-7,"domain_scores_codex":[0.9993474,0.00003465737,0.0001830822,0.0001660121,0.000105555,0.0001633273],"domain_scores_gemma":[0.9996218,0.00008965548,0.00008856275,0.00009785123,0.0000582747,0.00004386336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003700911,0.000003205371,0.0002722964,0.0000441855,0.00001699679,2.824371e-7,0.000617991,0.9264683,0.07079557,0.0008078103,0.000178845,0.0007908466],"study_design_scores_gemma":[0.0002741754,0.0000176015,0.002408364,0.00005013591,0.00001569269,0.000002429799,0.0002914233,0.9949623,0.001000097,0.00004862436,0.0007613013,0.0001678242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1177483,0.00002605026,0.8805028,0.0006104687,0.0003512247,0.0003359887,0.000001211967,0.0002047531,0.000219164],"genre_scores_gemma":[0.9864157,0.00002086221,0.01320899,0.0001162613,0.00009709349,0.00001047594,0.00002456754,0.00002947055,0.00007656222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8686674,"threshold_uncertainty_score":0.5483513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02587517122558289,"score_gpt":0.2486044096513348,"score_spread":0.2227292384257519,"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."}}