Computational Uncertainty

Computational uncertainty focuses on quantifying and managing uncertainty arising from both inherent randomness in data and limitations in computational methods used for analysis and prediction. Current research emphasizes integrating probabilistic approaches, such as Bayesian methods and conformal prediction, with deep learning models and other algorithms (e.g., sequential Monte Carlo, Gaussian processes) to provide more reliable uncertainty estimates. This improved understanding of uncertainty is crucial for enhancing the robustness and trustworthiness of machine learning models across diverse applications, from engineering system identification to historical linguistics and medical AI.

Papers

November 1, 2024

Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
Gaussian Process Scientific Inference RSD Difference of Gaussian Model Selection Computational Uncertainty

June 5, 2024

Robust Communication and Computation using Deep Learning via Joint Uncertainty Injection
Robert-Jeron Reifert, Hayssam Dahrouj, Alaa Alameer Ahmad, Haris Gacanin, Aydin Sezgin
Deep Learning Deep Neural Network Computation Method Robust Communication Delay Resilient Computational Uncertainty Uncertainty Injection

April 19, 2024

Probabilistic Numeric SMC Sampling for Bayesian Nonlinear System Identification in Continuous Time
Joe D. Longbottom, Max D. Champneys, Timothy J. Rogers
Nonlinear Dynamic Continuous Time Sequential Monte Carlo Computational Uncertainty Joint State

October 19, 2023

Representing and Computing Uncertainty in Phonological Reconstruction
Johann-Mattis List, Nathan W. Hill, Robert Forkel, Frederic Blum
Historical Linguistics Linguistic Data Probabilistic Reconstruction Phonological Reconstruction Computational Uncertainty

June 1, 2023

Quantifying Deep Learning Model Uncertainty in Conformal Prediction
Hamed Karimi, Reza Samavi
Uncertainty Quantification Conformal Prediction Model Uncertainty Predictive Uncertainty Computational Uncertainty Deep Learning Model Uncertainty

March 14, 2023

Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models
Guoxuan Xia, Christos-Savvas Bouganis
Deep Learning Uncertainty Estimation Deep Ensemble Single Model Early Exit Network Computational Uncertainty

November 11, 2022

The Implicit Delta Method
Nathan Kallus, James McInerney
Uncertainty Quantification Epistemic Uncertainty Quantification Delta Method Computational Uncertainty

May 30, 2022

Posterior and Computational Uncertainty in Gaussian Processes
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
Gaussian Process Covariance Conditioning Posterior Mode Computational Uncertainty Posterior Covariance

March 30, 2022

Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization
Marc Goerigk, Jannis Kurtz
Data Driven Lower Bound Multi Scenario Computational Uncertainty