Uncertainty Quantification

Also known as UQ

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44 papers in the last four weeks, up 110% on the four weeks before. 0.4% of all new papers.

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Statistical Taylor Expansion: A New and Path-Independent Method for Uncertainty Analysis
Oct 2, 2024Chengpu WangUncertainty Quantification Uncertainty Propagation
Grossman Street, Melville, NY 11747, USA
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Nov 15, 2023Ivo Pascal de Jong, Andreea Ioana Sburlea, Matias Valdenegro-ToroUncertainty Quantification
Department of Artificial Intelligence, Bernoulli Institute, University of Groningen, Groningen, The Netherlands
Training-Free Uncertainty Estimation for Embedding Models
May 31, 2023Young-Jin Park, Addison Kristanto Julistiono, Hao Wang +2Representation Learning Uncertainty Quantification
Massachusetts Institute of Technology · MIT-IBM Watson AI Lab
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations
Dec 23, 2018Balázs Csanád Csáji, Krisztián Balázs KisConfidence Region Estimation Uncertainty Quantification
EPIC Centre of Excellence MTA SZTAKI: Institute for Computer Science and Control Hungarian Academy of Sciences, Budapest, Hungary
Analytical Standard Errors for Exploratory Factor Solutions
Nov 12, 2018Xingwei Hu, Caihong Hu, Cheng-Kuang WuUncertainty Quantification Dimensionality Reduction
International Monetary Fund, Washington, DC, 20431, USA · Zhangjiajie College, Zhangjiajie, Hunan, 427000, China · Zhejiang Industry and Trade Vocational College, Wenzhou, Zhejiang, 325700, China
Estimating Uncertain Spatial Relationships in Robotics
Mar 27, 2013Randall Smith, Matthew Self, Peter CheesemanUncertainty Quantification Robot State Estimation
SRI International 333 Ravenswood Avenue Menlo Park, California 94025 · General Motors Research Laboratories Warren, Michigan · UC Berkeley +1
Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Date pendingRachel Longjohn, Giri Gopalan, Emily CasletonUncertainty Quantification
University of California Irvine · Los Alamos National Laboratory
Kalman Filtering Based Flight Management System Modeling for AAM Aircraft
Date pendingBalram Kandoria, Aryaman Singh SamyalKalman Filtering Uncertainty Quantification
SkyGrid, Austin, TX, 78727
Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification
Date pendingYilin Zhang, Cai Xu, Haishun Chen +2Evidential Deep Learning Uncertainty Quantification
Xidian University Xi’an, China
Decomposing LLM-Judge Uncertainty to Target Expert Labels
Date pendingRyan LailAnnotator Disagreement LLM-as-a-Judge
Composo AI
ActMap: Single-Pass Uncertainty Quantification from Generation-Time Activation Maps
Date pendingJacopo Dardini (University of Bologna), Roberta Calegari (University of Bologna)Uncertainty Quantification Confidence Estimation in Language Models
University of Bologna, Italy

Oct 2, 2024, stat.COJ/K move · Enter open · S save

Statistical Taylor Expansion: A New and Path-Independent Method for Uncertainty Analysis

Chengpu Wang

Grossman Street, Melville, NY 11747, USA

Uncertainty Quantification Uncertainty Propagation

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Statistical Taylor expansion is a rigorous extension of conventional Taylor expansion that replaces each precise input variable with a random variable of known distribution and sample count, then computes the mean, deviation, and a bounding reliability of every result. By tracking the propagation of input uncertainties through all intermediate steps, it renders the final result path-independent, with precise quantification of the tracking quality. This path-independence sets it fundamentally apart from conventional numerical approaches, which are path-dependent. This study presents an implementation called variance arithmetic and demonstrates its performance across diverse mathematical applications. This study also reveals the potentially substantial impact of numerical errors in library functions, the defect of applying input uncertainties as weights in conventional regression, and the modeling error of the discrete Fourier transformation.

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