{"id":"W3175907392","doi":"","title":"On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness","year":2021,"lang":"en","type":"article","venue":"Conference on Learning Theory","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Monte Carlo method; Smoothness; Lipschitz continuity; Rate of convergence; Physics; Combinatorics; BETA (programming language); Convex function; Distribution (mathematics); Mathematics; Regular polygon; Mathematical analysis; Statistics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002022161,0.0001741058,0.0002998108,0.00003460348,0.0001686197,0.00004612802,0.0003159696,0.00008299633,0.0001635069],"category_scores_gemma":[0.004775897,0.00009722161,0.0000834925,0.0001191934,0.0002649256,0.00003113018,0.0001566939,0.0005371508,8.369356e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001311834,"about_ca_system_score_gemma":0.00006512149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001534639,"about_ca_topic_score_gemma":0.00001062658,"domain_scores_codex":[0.9977325,0.001376483,0.0002335696,0.0002525774,0.0002109264,0.0001939393],"domain_scores_gemma":[0.9928843,0.006288538,0.0001802512,0.0004404575,0.0001543285,0.00005215336],"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":[0.00005867048,0.00002659867,0.001655739,0.00008452454,0.00006340718,0.000008183723,0.003477806,0.000005453263,0.0009866132,0.9852289,0.0004054202,0.007998696],"study_design_scores_gemma":[0.001884579,0.001266838,0.006460828,0.003116296,0.0005088099,0.00003645274,0.04703264,0.004169766,0.06220546,0.8616377,0.01025069,0.001429924],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574653,0.0002106928,0.007620903,0.001686807,0.0001785271,0.0002080825,0.00001369225,0.00005081562,0.03256521],"genre_scores_gemma":[0.9950996,0.0001166624,0.0002989604,0.0002284081,0.0000537985,0.00001967493,0.000001788348,0.00002242062,0.00415871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1235912,"threshold_uncertainty_score":0.5717539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06749056405776141,"score_gpt":0.3460512910546785,"score_spread":0.2785607269969171,"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."}}