Uncertainty Quantification

Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA ACL 2023 Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model AAAI 2023 Predictive Multiplicity in Probabilistic Classification AAAI 2023 Principled Out-of-Distribution Detection via Multiple Testing JMLR 2023 Probabilistic Conformal Prediction Using Conditional Random Samples AISTATS 2023 Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval NeurIPS 2023 PWSHAP: A Path-Wise Explanation Model for Targeted Variables ICML 2023 RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data EMNLP 2023 Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts ICML 2023 Reliability in Semantic Segmentation: Are We on the Right Track? CVPR 2023 Retrospective Uncertainties for Deep Models using Vine Copulas AISTATS 2023 Revisiting inference after prediction JMLR 2023 Risk Bounds on Aleatoric Uncertainty Recovery AISTATS 2023 R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents ICML 2023 Selection by Prediction with Conformal p-values JMLR 2023 Selectively Answering Ambiguous Questions EMNLP 2023 Sequential Predictive Conformal Inference for Time Series ICML 2023 Sharp Calibrated Gaussian Processes NeurIPS 2023 SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process ICML 2023 Stochastic Segmentation with Conditional Categorical Diffusion Models ICCV 2023 Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data ICML 2023 The Unreasonable Effectiveness of Deep Evidential Regression AAAI 2023 TiDAL: Learning Training Dynamics for Active Learning ICCV 2023 Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration CVPR 2023 Transfer Knowledge From Head to Tail: Uncertainty Calibration Under Long-Tailed Distribution CVPR 2023