{"id":"W3046037042","doi":"","title":"Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence.","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Divergence (linguistics); Marginal distribution; Domain (mathematical analysis); USable; Matching (statistics); Computer science; Kullback–Leibler divergence; Perspective (graphical); Chain rule (probability); Mathematics; Information theory; Upper and lower bounds; Theoretical computer science; Mathematical optimization; Artificial intelligence; Statistics; Random variable; Regular conditional probability; Posterior probability; Bayesian probability; Mathematical analysis","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":[0.008749349,0.001276686,0.001331607,0.001427382,0.001067692,0.00319408,0.002796166,0.002755055,0.002770347],"category_scores_gemma":[0.03358838,0.0006442564,0.001121584,0.001338473,0.005919663,0.006211403,0.006413385,0.007320668,0.0006205193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002843424,"about_ca_system_score_gemma":0.002035968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002001934,"about_ca_topic_score_gemma":0.001503196,"domain_scores_codex":[0.9960369,0.001991663,0.0001480534,0.0005998032,0.001013476,0.0002101146],"domain_scores_gemma":[0.9831618,0.01267955,0.0008519813,0.002039297,0.0008050865,0.0004623778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005411377,0.00005957979,0.00117297,0.0001560307,0.00008539507,0.0001203931,0.0002243946,0.1823095,0.001359934,0.7784467,0.003166221,0.03284481],"study_design_scores_gemma":[0.000007132347,0.00003526429,0.0002997646,0.00003326951,0.000009848808,0.00006719709,0.00003007944,0.4694222,0.000718058,0.5276812,0.001678364,0.00001767928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005482311,0.0007407116,0.9871175,0.001052014,0.00007046775,0.00003707024,0.00006398125,0.00011582,0.005320091],"genre_scores_gemma":[0.7206469,0.002153152,0.2647774,0.00188955,0.0006007288,0.0004590529,0.0003890716,0.0003618884,0.008722322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008749349,"threshold_uncertainty_score":0.04627156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06600459547504262,"score_gpt":0.1885956435310878,"score_spread":0.1225910480560452,"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."}}