Data Uncertainty

Data uncertainty, encompassing both inherent randomness in data (aleatoric uncertainty) and limitations in model knowledge (epistemic uncertainty), is a critical challenge across diverse fields. Current research focuses on developing methods to quantify and model this uncertainty, often employing Bayesian frameworks, ensemble methods, and techniques like Monte Carlo dropout within deep learning architectures. Accurate uncertainty quantification is crucial for improving the reliability and trustworthiness of machine learning models, particularly in high-stakes applications like medical diagnosis and autonomous systems, enabling more informed decision-making and risk assessment. This improved reliability is achieved through techniques that explicitly incorporate uncertainty into model training and inference, leading to more robust and reliable predictions.

Papers

September 1, 2024

Uncertainty-oriented Order Learning for Facial Beauty Prediction
Xuefeng Liang, Zhenyou Liu, Jian Lin, Xiaohui Yang, Takatsune Kumada
Face Image Data Uncertainty Facial Beauty Prediction

August 13, 2024

MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty
Yongjin Yang, Haneul Yoo, Hwaran Lee
Large Language Model Medical LLM Uncertainty Quantification Quantum Algorithm Model Uncertainty Data Uncertainty

July 29, 2024

Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals
Sergio Caprioli, Jacopo Foschi, Riccardo Crupi, Alessandro Sabatino
Machine Learning Raw Data Imputation Algorithm Prediction Interval Imputation Method Incomplete Data Data Uncertainty Environmental, Social, and Governance Imputation Strategy

June 20, 2024

Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning
Asaf Ben Arie, Malka Gorfine
Deep Learning Uncertainty Quantification Prediction Uncertainty Confidence Interval Data Uncertainty Reliable Uncertainty Confidence Band

June 17, 2024

Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications
Ivan Donadello, Paolo Felli, Craig Innes, Fabrizio Maria Maggi, Marco Montali
Temporal Logic Data Uncertainty Fuzzy Trace Conformance Checking Temporal Logical Rule

May 28, 2024

Learning from Uncertain Data: From Possible Worlds to Possible Models
Jiongli Zhu, Su Feng, Boris Glavic, Babak Salimi
LeArning Abstract Gradient Descent Prediction Uncertainty Data Uncertainty Diverse Model Optimal Model Predictive Multiplicity Possible World

March 31, 2024

Mining Weighted Sequential Patterns in Incremental Uncertain Databases
Kashob Kumar Roy, Md Hasibul Haque Moon, Md Mahmudur Rahman, Chowdhury Farhan Ahmed, Carson Kai-Sang Leung
Mining Complex Data Uncertainty Sequential Pattern Frequent Itemset

January 17, 2024

Uncertainty Modeling in Ultrasound Image Segmentation for Precise Fetal Biometric Measurements
Shuge Lei
Data Uncertainty Ultrasound Image Segmentation Uncertainty Modeling Ultrasound Data Fetal Biometry Fetal Motion

December 19, 2023

Observation-Augmented Contextual Multi-Armed Bandits for Robotic Exploration with Uncertain Semantic Data
Shohei Wakayama, Nisar Ahmed
Autonomous Exploration Active Inference Contextual Multi Armed Bandit Data Uncertainty Robot Decision Bandit Learning

December 16, 2023

One step closer to unbiased aleatoric uncertainty estimation
Wang Zhang, Ziwen Ma, Subhro Das, Tsui-Wei Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
Deep Learning Aleatoric Uncertainty High Uncertainty Value Sample STEP Data Uncertainty Variance Reduction Technique

December 13, 2023

Training of Neural Networks with Uncertain Data: A Mixture of Experts Approach
Lucas Luttner
Neural Network Training Data Mixture Component Expert Knowledge Aleatoric Uncertainty Data Uncertainty Input Uncertainty

November 29, 2023

Robustness Approaches for the Examination Timetabling Problem under Data Uncertainty
Bernd Bassimir, Rolf Wanka
Robust Optimization Data Uncertainty General Robustness Instance Generation Examination Timetabling Problem

September 18, 2023

Distributional Estimation of Data Uncertainty for Surveillance Face Anti-spoofing
Mouxiao Huang
Face Anti Spoofing Data Uncertainty

May 22, 2023

Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation
Zhu Liu, Ying Liu
Language Understanding High Uncertainty Anticipation Uncertainty Estimation Structural Ambiguity Sense Disambiguation Data Uncertainty Uncertainty Score

March 29, 2023

ALUM: Adversarial Data Uncertainty Modeling from Latent Model Uncertainty Compensation
Wei Wei, Jiahuan Zhou, Hongze Li, Ying Wu
Model Uncertainty Uncertainty Aware Data Uncertainty Uncertainty Modeling Adversarial Uncertainty

February 26, 2023

A Comprehensive Survey on Uncertainty Quantification for Deep Learning
Wenchong He, Zhe Jiang
Deep Learning Deep Neural Network Uncertainty Quantification Comprehensive Survey Model Uncertainty Data Uncertainty DNN Prediction

January 16, 2023

Modeling Uncertain Feature Representation for Domain Generalization
Xiaotong Li, Zixuan Hu, Jun Liu, Yixiao Ge, Yongxing Dai, Ling-Yu Duan
Deep Learning Model Domain Generalization Domain Shift Data Uncertainty Feature Uncertainty Statistical Feature

January 15, 2023

Max-min Learning of Approximate Weight Matrices from Fuzzy Data
Ismaïl Baaj
Weight Matrix Data Uncertainty Fuzzy Relation Approximate Solution

December 2, 2022

Improving Training and Inference of Face Recognition Models via Random Temperature Scaling
Lei Shang, Mouxiao Huang, Wu Shi, Yuchen Liu, Yang Liu, Fei Wang, Baigui Sun, Xuansong Xie, Yu Qiao
Scientific Inference Face Recognition Face Recognition Model Temperature Scaling Data Uncertainty Uncertainty Modeling Improved Training

October 24, 2022

Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval
Zhaopeng Dou, Zhongdao Wang, Weihua Chen, Yali Li, Shengjin Wang
Uncertainty Aware Uncertain Reasoning Data Uncertainty Predictive Reliability Text Based Person Retrieval Uncertainty Learning Low Quality Sample