Uncertainty Quantification

Uncertainty quantification (UQ) aims to assess and represent the confidence in predictions made by machine learning models, crucial for high-stakes applications where reliable predictions are paramount. Current research focuses on developing robust UQ methods, particularly addressing biases in predictions and efficiently quantifying uncertainty in large language models and deep neural networks, often employing techniques like conformal prediction, Bayesian methods, and ensemble learning. The ability to reliably quantify uncertainty enhances the trustworthiness and applicability of machine learning across diverse fields, from healthcare diagnostics and autonomous driving to climate modeling and drug discovery.

Papers

August 24, 2023

Probabilistic load forecasting with Reservoir Computing
Michele Guerra, Simone Scardapane, Filippo Maria Bianchi
Uncertainty Quantification Reservoir Computing Probabilistic Load Point Forecasting

August 23, 2023

Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification
Benjamin Lambert, Pauline Roca, Florence Forbes, Senan Doyle, Michel Dojat
Uncertainty Quantification Tumor Segmentation Cancer Classification Liver Tumor Segmentation Liver Tumor Contrast Enhanced Magnetic Resonance Imaging Hybrid Network Ce Mri

August 21, 2023

Deep Evidential Learning for Bayesian Quantile Regression
Frederik Boe Hüttel, Filipe Rodrigues, Francisco Câmara Pereira
Uncertainty Quantification Epistemic Uncertainty Quantile Regression Non Gaussian Accurate Uncertainty

August 20, 2023

Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling
Chen Zhao, Michele Esposito, Zhihui Xu, Weihua Zhou
Semantic Segmentation Uncertainty Quantification Coronary Artery Hypergraph Matching

August 18, 2023

August 17, 2023

Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Xuanlong Yu, Gianni Franchi, Jindong Gu, Emanuel Aldea
Native Robustness Uncertainty Quantification Novel Regression Auxiliary Objective Robust Uncertainty Dirichlet Prior

August 11, 2023

August 6, 2023

Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing
Shuang Ao
Artificial Intelligence Uncertainty Quantification Model Uncertainty Accurate Uncertainty System Safety Safety Critical Task

August 5, 2023

Towards the Development of an Uncertainty Quantification Protocol for the Natural Gas Industry
Babajide Kolade
Uncertainty Quantification Uncertainty Estimation Development Activity Mechanistic Model Uncertainty Information Gas Industry

July 31, 2023

Conformal PID Control for Time Series Prediction
Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani
Uncertainty Quantification Conformal Prediction Time Series Prediction Online Conformal Prediction Prediction Rule Conformal Predictive

July 24, 2023

Model-free generalized fiducial inference
Jonathan P Williams
Uncertainty Quantification Model Free Predictive Inference

July 19, 2023

July 18, 2023

July 11, 2023

Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction
Suk Yee Yong, Cheng Soon Ong
Uncertainty Quantification Conformal Prediction Black Hole

July 5, 2023

Conditional Korhunen-Lo\'{e}ve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling
Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky
Uncertainty Quantification Surrogate Model High Dimensional Problem Inverse Modeling Conditional Model Permeability Field Karhunen Lo\`eve Transform Heterogeneous Parameter

July 3, 2023

Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models
Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
Uncertainty Quantification Question Answering Information Redundancy Unstructured Text Relative Relevance Autoregressive LLM Predictive Uncertainty Quantification Attention Transfer

Uncertainty Quantification

Papers

Probabilistic load forecasting with Reservoir Computing

Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification

Deep Evidential Learning for Bayesian Quantile Regression

Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling

Quantifying Uncertainties of Contact Classifications in a Human-Robot Collaboration with Parallel Robots

Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction

Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression

Uncertainty Quantification for Image-based Traffic Prediction across Cities

Comparing the quality of neural network uncertainty estimates for classification problems

Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing

Towards the Development of an Uncertainty Quantification Protocol for the Natural Gas Industry

Conformal PID Control for Time Series Prediction

Model-free generalized fiducial inference

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

Towards Reliable Rare Category Analysis on Graphs via Individual Calibration

Physics-based Reduced Order Modeling for Uncertainty Quantification of Guided Wave Propagation using Bayesian Optimization

Conformal prediction under ambiguous ground truth

Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction

Conditional Korhunen-Lo\'{e}ve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling

Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models