probability calibration

Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification ICML 2021 Local Temperature Scaling for Probability Calibration ICCV 2021 Meta-Cal: Well-controlled Post-hoc Calibration by Ranking ICML 2021 On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search EACL 2021 Reliable Decisions with Threshold Calibration NeurIPS 2021 Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right EMNLP 2021 Improving Confidence Estimates for Unfamiliar Examples CVPR 2020 Multivariate Probability Calibration with Isotonic Bernstein Polynomials IJCAI 2020 Non-Parametric Calibration for Classification AISTATS 2020 Posterior Calibrated Training on Sentence Classification Tasks ACL 2020 Probability Calibration for Knowledge Graph Embedding Models ICLR 2020 Transferable Calibration with Lower Bias and Variance in Domain Adaptation NeurIPS 2020 Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration NeurIPS 2019 Calibrating DNN Posterior Probability Estimates of HMM/DNN Models to Improve Social Signal Detection from Audio Data Interspeech 2019 Verified Uncertainty Calibration NeurIPS 2019 Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers AISTATS 2017 On the Interpretability of Conditional Probability Estimates in the Agnostic Setting AISTATS 2017 Probability Calibration Trees ACML 2017 Bayesian Networks: a Combined Tuning Heuristic PGM 2016 Calibrated Structured Prediction NeurIPS 2015 Proper losses for learning from partial labels NeurIPS 2012 Does an Efficient Calibrated Forecasting Strategy Exist? COLT 2011