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

January 24, 2024

Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification
Hyun-Suk Lee, Do-Yup Kim, Kyungsik Min
Deep Learning Uncertainty Quantification Self Improving Interference Management

January 23, 2024

Benchmarking LLMs via Uncertainty Quantification
Fanghua Ye, Mingming Yang, Jianhui Pang, Longyue Wang, Derek F. Wong, Emine Yilmaz, Shuming Shi, Zhaopeng Tu
Medical LLM Uncertainty Quantification Open Source Large Language Model Evaluation Platform Scale LLM

January 22, 2024

Sparse discovery of differential equations based on multi-fidelity Gaussian process
Yuhuang Meng, Yue Qiu
Uncertainty Quantification Surrogate Model Differential Equation Gaussian Process Regression Sparse Identification Multi Fidelity Gaussian Process Sparse Model Discovery

January 17, 2024

High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
Wanrong Zhu, Zhipeng Lou, Ziyang Wei, Wei Biao Wu
Uncertainty Quantification Inference Efficiency Confidence Interval Parallel Stochastic

January 13, 2024

Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation
Mauricio Rivera, Jean-François Godbout, Reihaneh Rabbany, Kellin Pelrine
Uncertainty Quantification Misinformation Mitigation Confidence Elicitation Sample Based

January 12, 2024

Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Geethen Singh, Glenn Moncrieff, Zander Venter, Kerry Cawse-Nicholson, Jasper Slingsby, Tamara B Robinson
Uncertainty Quantification Conformal Prediction Pixel Wise Uncertainty Unreliable Prediction Conformal Predictor Probabilistic Machine Learning

January 8, 2024

Dual-Channel Reliable Breast Ultrasound Image Classification Based on Explainable Attribution and Uncertainty Quantification
Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Jijun Tang, Yan Tong
Uncertainty Quantification Exact Inference Breast Ultrasound Attribution Based Guided Image Synthesis Predictive Reliability

January 7, 2024

Uncertainty Quantification on Clinical Trial Outcome Prediction
Tianyi Chen, Yingzhou Lu, Nan Hao, Yuanyuan Zhang, Capucine Van Rechem, Jintai Chen, Tianfan Fu
Uncertainty Quantification Model Uncertainty Trial Outcome Prediction

December 30, 2023

December 28, 2023

Uncertainty Quantification in Machine Learning for Joint Speaker Diarization and Identification
Simon W. McKnight, Aidan O. T. Hogg, Vincent W. Neo, Patrick A. Naylor
Machine Learning Uncertainty Quantification Person Identification Speaker Diarization Diarization Error Rate

December 25, 2023

Efficient Conformal Prediction under Data Heterogeneity
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horvath, Martin Takac, Eric Moulines, Maxim Panov
Uncertainty Quantification Conformal Prediction Data Heterogeneity Confidence Set Personalized Prediction

December 21, 2023

Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions
Sankalp Gilda
Uncertainty Quantification Conformal Prediction Spectral Energy Distribution Galaxy Formation Robust Fitting

December 16, 2023

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Shunsuke Horii, Yoichi Chikahara
Gaussian Process Uncertainty Quantification Treatment Effect Heterogeneous Treatment Effect Prior Distribution Semi Parametric Heterogeneous Treatment Effect Estimation Bayesian Inference Framework

December 12, 2023

December 11, 2023

December 8, 2023

Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
Philipp Reiser, Javier Enrique Aguilar, Anneli Guthke, Paul-Christian Bürkner
Uncertainty Quantification Surrogate Modeling Uncertainty Propagation Fast Propagation Probabilistic Surrogate Uncertainty Aware Surrogate Model

December 5, 2023

Neural parameter calibration and uncertainty quantification for epidemic forecasting
Thomas Gaskin, Tim Conrad, Grigorios A. Pavliotis, Christof Schütte
Uncertainty Quantification Markov Chain Monte Carlo Epidemic Forecasting Neural Network Calibration Contagion Effect

Uncertainty Quantification

Papers

Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification

Benchmarking LLMs via Uncertainty Quantification

Sparse discovery of differential equations based on multi-fidelity Gaussian process

High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization

Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation

Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

Dual-Channel Reliable Breast Ultrasound Image Classification Based on Explainable Attribution and Uncertainty Quantification

Uncertainty Quantification on Clinical Trial Outcome Prediction

Second-Order Uncertainty Quantification: Variance-Based Measures

DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling

Uncertainty Quantification in Machine Learning for Joint Speaker Diarization and Identification

Efficient Conformal Prediction under Data Heterogeneity

Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification

Identifying Drivers of Predictive Aleatoric Uncertainty

Uncertainty quantification in automated valuation models with locally weighted conformal prediction

Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems

Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference

Neural parameter calibration and uncertainty quantification for epidemic forecasting