{"id":"W2963204788","doi":"10.5555/3310435.3310519","title":"Proportional volume sampling and approximation algorithms for A-optimal design","year":2019,"lang":"en","type":"article","venue":"Symposium on Discrete Algorithms","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Approximation algorithm; Mathematics; Matrix (chemical analysis); Mathematical optimization; Algorithm; Measure (data warehouse); Applied mathematics; Computer science","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":[0.006599403,0.002827501,0.002791395,0.001934686,0.0008011616,0.002028229,0.00213399,0.002413229,0.003570881],"category_scores_gemma":[0.03362402,0.001557834,0.001690984,0.002041635,0.002514323,0.002739428,0.002917155,0.003584812,0.0008687634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002186963,"about_ca_system_score_gemma":0.001975533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004975016,"about_ca_topic_score_gemma":0.003372738,"domain_scores_codex":[0.9957159,0.002232044,0.0002264665,0.0005504371,0.001010548,0.000264455],"domain_scores_gemma":[0.9770859,0.01926722,0.0008723878,0.001328503,0.001093298,0.000352708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002095493,0.0001112034,0.0008450045,0.0002072852,0.00008981139,0.00006886302,0.0001241872,0.8618993,0.001000975,0.08159126,0.001602966,0.05224963],"study_design_scores_gemma":[0.00002376842,0.00004046543,0.00004068646,0.0000171622,0.000008168284,0.000019057,0.000009136117,0.96422,0.0002549854,0.03470606,0.0006539715,0.000006509914],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001794439,0.0003497404,0.9966666,0.0001283501,0.00003505979,0.00004250132,0.00002189248,0.000131164,0.0008302777],"genre_scores_gemma":[0.1951407,0.00096594,0.7988006,0.000499476,0.0002196841,0.0007529093,0.0003514492,0.0002420531,0.003027038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006599403,"threshold_uncertainty_score":0.03490138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02753062799451619,"score_gpt":0.2601438406716673,"score_spread":0.2326132126771512,"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."}}