predictive distribution

DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs ACL 2026 Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens ACL 2026 A Unified Evaluation Framework for Epistemic Predictions AISTATS 2025 Credibility-Aware Multimodal Fusion Using Probabilistic Circuits AISTATS 2025 Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles ICLR 2025 Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels AISTATS 2025 On Information-Theoretic Measures of Predictive Uncertainty UAI 2025 Quantile Regression with Large Language Models for Price Prediction ACL 2025 Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization JMLR 2025 Knowledge Distillation with Auxiliary Variable ICML 2024 LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language NeurIPS 2024 Posterior Uncertainty Quantification in Neural Networks using Data Augmentation AISTATS 2024 Probabilistic Calibration by Design for Neural Network Regression AISTATS 2024 Probabilistic reconciliation of mixed-type hierarchical time series UAI 2024 Quasi-Bayes meets Vines NeurIPS 2024 Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees NeurIPS 2024 Estimating Regression Predictive Distributions with Sample Networks AAAI 2023 Generative Calibration for In-context Learning EMNLP 2023 GFlowOut: Dropout with Generative Flow Networks ICML 2023 Kalman Bayesian Neural Networks for Closed-Form Online Learning AAAI 2023 Martingale Posterior Neural Processes ICLR 2023 Maximum likelihood estimation in Gaussian process regression is ill-posed JMLR 2023 Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates UAI 2023 A Predictive Approach to Bayesian Nonparametric Survival Analysis AISTATS 2022 Evaluating high-order predictive distributions in deep learning UAI 2022