{"id":"W4283327156","doi":"10.1109/lra.2022.3179424","title":"Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Weighting; Residual; Gaussian; Computer science; Mathematical optimization; Mathematics; Norm (philosophy); Mode (computer interface); Convergence (economics); Multivariate statistics; Econometrics; Algorithm; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002789668,0.001264824,0.00110751,0.0006481135,0.0003072368,0.0009455123,0.002077715,0.001095564,0.001536835],"category_scores_gemma":[0.009388673,0.0004906756,0.0006046102,0.0007486705,0.001173381,0.002316636,0.00317596,0.002476646,0.0006849183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005160486,"about_ca_system_score_gemma":0.0009640657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001373601,"about_ca_topic_score_gemma":0.0009724629,"domain_scores_codex":[0.9985739,0.0005037893,0.00006005368,0.0002162699,0.0005476486,0.00009835987],"domain_scores_gemma":[0.9975144,0.001457011,0.0001895771,0.0003259479,0.0004148844,0.00009829636],"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":[0.0003679001,0.0001257526,0.001189249,0.0002543025,0.0001169227,0.0001314923,0.0001677798,0.662177,0.01420842,0.03327651,0.006809856,0.2811748],"study_design_scores_gemma":[0.000008935929,0.00003856541,0.0001088325,0.000006554777,0.000004057761,0.00002237832,0.000007617189,0.9905593,0.00154531,0.00678876,0.0009024651,0.000007237302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002636095,0.0001105383,0.9965791,0.000070678,0.00001970685,0.00001164132,0.00001310787,0.0002942452,0.0002649264],"genre_scores_gemma":[0.3119139,0.0005811402,0.6819324,0.0003878672,0.0002149366,0.0002730422,0.0003318163,0.0008258202,0.003539033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002789668,"threshold_uncertainty_score":0.01475334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0380937386487361,"score_gpt":0.239696786956887,"score_spread":0.2016030483081509,"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."}}