Uncertainty Quantification

June 26, 2022

Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection
Xiongjie Chen, Yunpeng Li, Yongxin Yang
Uncertainty Quantification Distribution Detection Distribution Data Efficient Ensemble

June 21, 2022

Uncertainty Quantification for Competency Assessment of Autonomous Agents
Aastha Acharya, Rebecca Russell, Nisar R. Ahmed
Uncertainty Quantification Autonomous Agent Epistemic Uncertainty Competency Assessment

June 19, 2022

Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
Zhaobin Mo, Yongjie Fu, Xuan Di
Generative Adversarial Network Uncertainty Quantification Physic Informed Deep Learning Traffic Model Traffic State Estimation Stochastic Traffic
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
Zhaobin Mo, Yongjie Fu, Daran Xu, Xuan Di
Generative Adversarial Network Uncertainty Quantification Physic Informed Deep Learning Physic Informed Flow

June 18, 2022

Nonparametric Multi-shape Modeling with Uncertainty Quantification
Hengrui Luo, Justin D. Strait
Gaussian Process Uncertainty Quantification Statistical Shape Linear Dependence Shape Analysis Planar Curve

June 17, 2022

June 12, 2022

Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Daiwei Zhang, Tianci Liu, Jian Kang
Uncertainty Quantification Latent Noise Predictive Density Density Regression

June 8, 2022

Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
Brian Staber, Sébastien Da Veiga
Uncertainty Quantification Bayesian Neural Network Evaluation Metric Posterior Inference Regression Task Posterior Approximation Kernel Stein Discrepancy

June 6, 2022

Robust Calibration with Multi-domain Temperature Scaling
Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan
Uncertainty Quantification Distribution Shift Temperature Scaling Robust Calibration

June 2, 2022

Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning
David Brandfonbrener, Remi Tachet des Combes, Romain Laroche
Offline Reinforcement Learning Uncertainty Quantification Uncertainty Estimation

May 30, 2022

May 29, 2022

Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)
Biprateep Dey, David Zhao, Brett H. Andrews, Jeffrey A. Newman, Rafael Izbicki, Ann B. Lee
Application Proficiency Uncertainty Quantification Probabilistic Forecast Conditional Density Predictive Inference Predictive Distribution Probability MAP Photometric Redshift

May 28, 2022

Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model
Jian Li, Dongxiao Zhang, Tianhao He, Qiang Zheng
Uncertainty Quantification Surrogate Model Multiphase Flow Data Driven Surrogate Porous Medium Subsurface Flow

May 20, 2022

The Unreasonable Effectiveness of Deep Evidential Regression
Nis Meinert, Jakob Gawlikowski, Alexander Lavin
Uncertainty Quantification Epistemic Uncertainty Uncertainty Aware Evidential Model Evidential Regression

May 19, 2022

Uncertainty Quantification for Transport in Porous media using Parameterized Physics Informed neural Networks
Cedric Fraces Gasmi, Hamdi Tchelepi
Neural Network Uncertainty Quantification Transportation System Porous Medium Reservoir Pressure Reservoir Model Probability Space

May 18, 2022

Achieving Risk Control in Online Learning Settings
Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano
Uncertainty Quantification Conformal Prediction Online Learning Online Learning Algorithm Risk Management Uncertainty Set

May 13, 2022

A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification
Cheng Chang, Tieyong Zeng
Uncertainty Quantification Gaussian Process Regression Boltzmann Distribution Deep Kernel Hybrid Physic Machine Learning

May 11, 2022

Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification
Steven Jorgensen, John Holodnak, Jensen Dempsey, Karla de Souza, Ananditha Raghunath, Vernon Rivet, Noah DeMoes, Andrés Alejos, Allan Wollaber
Uncertainty Quantification Encrypted Traffic Encrypted Traffic Classification

Papers

Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection

Uncertainty Quantification for Competency Assessment of Autonomous Agents

Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks

TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification

Nonparametric Multi-shape Modeling with Uncertainty Quantification

Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification

Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift

Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks

Benchmarking Bayesian neural networks and evaluation metrics for regression tasks

Robust Calibration with Multi-domain Temperature Scaling

Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning

Uncertainty Quantification for Fairness in Two-Stage Recommender Systems

Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning

Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)

Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model

The Unreasonable Effectiveness of Deep Evidential Regression

Uncertainty Quantification for Transport in Porous media using Parameterized Physics Informed neural Networks

Achieving Risk Control in Online Learning Settings

A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification

Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification