uncertainty quantification

Posterior Sampling for Deep Reinforcement Learning ICML 2023 Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model AAAI 2023 Practical Equivariances via Relational Conditional Neural Processes NeurIPS 2023 Predict-then-Calibrate: A New Perspective of Robust Contextual LP NeurIPS 2023 Probabilistically robust conformal prediction UAI 2023 Probabilistic circuits that know what they don’t know UAI 2023 Probabilistic Concept Bottleneck Models ICML 2023 Probabilistic Conformal Prediction Using Conditional Random Samples AISTATS 2023 Probabilistic Exponential Integrators NeurIPS 2023 Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation ICCV 2023 Probabilistic Shape Models of Anatomy Directly from Images AAAI 2023 Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantisation. NeurIPS 2023 ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images AISTATS 2023 Proper Scoring Rules for Survival Analysis ICML 2023 Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval NeurIPS 2023 Quantification of Uncertainty with Adversarial Models NeurIPS 2023 Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? UAI 2023 RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data EMNLP 2023 Reliable Gradient-free and Likelihood-free Prompt Tuning EACL 2023 Representation Uncertainty in Self-Supervised Learning as Variational Inference ICCV 2023 Representing and Computing Uncertainty in Phonological Reconstruction EMNLP 2023 Revisiting Rotation Averaging: Uncertainties and Robust Losses CVPR 2023 Risk Bounds on Aleatoric Uncertainty Recovery AISTATS 2023 Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners CoRL 2023 Robust and Adaptive Deep Learning via Bayesian Principles AAAI 2023