Tag: Uncertainty Quantification
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Distance-Based Quantification of Credal Uncertainty
Discover a novel distance-based approach to quantify aleatoric and epistemic uncertainty in credal sets for multiclass classification.
Uncertainty-Aware XAI: A Systematic Survey & Insights
Explore a comprehensive survey on uncertainty-aware XAI, covering methods, evaluation challenges, and future directions for reliable AI explanations.
KMM-CP: Reliable Conformal Prediction Under Covariate Shift
Discover KMM-CP, a novel method using Kernel Mean Matching to improve conformal prediction accuracy under covariate shift in machine learning.
Generative Score Inference for Reliable Multimodal AI
Discover Generative Score Inference (GSI) for accurate uncertainty quantification in multimodal data, enhancing AI reliability in images and text tasks.
Heteroscedasticity in Probabilistic Time Series Forecasting
Discover how embracing heteroscedasticity with LSG-VAE improves accuracy and uncertainty in probabilistic time series forecasting.