Regression Uncertainty Estimation

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26 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Practical Non-Stationary Graph Gaussian Processes
Nov 6, 2026K. S. Sesh Kumar, Viacheslav BorovitskiyGaussian Processes Regression Uncertainty Estimation
Brevan Howard Centre for Financial Analysis, Imperial Business School · School of Informatics, University of Edinburgh
A trust layer for fast pedestal-stability surrogates: calibrated error bars, out-of-distribution alarms and few-shot recalibration
Nov 4, 2026Amin VakhshouriRegression Uncertainty Estimation PDE Surrogate Modeling
dataquanta LLC, Delaware, USA
Correctness and Predictive Uncertainty in Bayesian TSK Fuzzy Regression: A Reproducible Evaluation on Tabular and Building-Meter Data
Nov 1, 2026Zhiren XiaoRegression Uncertainty Estimation Fuzzy Logic
Guangdong University of Finance, Guangzhou, China
Confidence-Aware Speed-over-Ground Estimation from Doppler Spectra via Reconstruction-Based Representation Learning
Oct 29, 2026Malte Stelzer, Marvin Sach, Timo Bartels +5Regression Uncertainty Estimation Robust Estimation
Technische Universität Braunschweig, Germany · Siemens Mobility GmbH, Berlin, Germany
Grounding Predictive Reliability through Masked Observability
Oct 29, 2026Shang Gao, Bernhard Sick, Franz Götz-HahnUncertainty Quantification Regression Uncertainty Estimation
University of Kassel, Germany
Uncertainty Quantification of Next Generation Reservoir Computing with Applications to Memory-Driven Dynamical Systems
Sep 26, 2026Livia Popa, Sumanta Basu, Martin T. WellsUncertainty Quantification Regression Uncertainty Estimation
Department of Statistics and Data Science, Cornell University, New York, USA
The segmentation ceiling: why explicit left-ventricular masks do not improve learned ejection-fraction regression
Sep 17, 2026Farshid Farhadi Khouzani, Paul La Plante, Bryar Mustafa Shareef +1Left Ventricular Ejection Fraction Estimation Echocardiography
Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV, USA
A Ground-Truth Framework for Uncertainty Disentanglement with Posterior Risk
Aug 6, 2026Frieder Wizgall, Georg Tirpitz, Moritz Seiler +2Uncertainty Quantification Aleatoric Uncertainty
University of Tübingen, Tübingen, Germany · Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany · Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany +2
Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery
Aug 3, 2026Laura Bader, Muhammad Ammar Ahmed, Xiao Xiang Zhu +1Remote Sensing Image Understanding Evidential Deep Learning
LMU Munich, 80539 Munich, Germany · Munich Center for Machine Learning (MCML) · TU Munich, 80333 Munich
Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation
Jul 13, 2026Ritu Yadav, Andrea Nascetti, Yifang BanUncertainty Quantification Forest Aboveground Biomass Estimation
Ritu Yadav, Andrea Nascetti and Yifang Ban is with Division of Geoinformatics, KTH Royal Institute of Technology, Sweden.
Efficient Analytic Uncertainty Quantification for Multi-Modal Regression
Jun 23, 2026Kun Jin, James Harrison, Jiawei Li +8Quantile Regression Uncertainty Quantification
1Google · 2Google DeepMind
On the QUEST for Uncertainty Quantification via Highest Density Regions
Jun 17, 2026Sam Goring, Tom Kuipers, Nicola Paoletti +1Uncertainty Quantification Aleatoric Uncertainty
King’s College London · Northeastern University London
SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors
Jun 9, 2026Soundouss Messoudi, Sylvain Rousseau, Sébastien DesterckeConformal Prediction Adaptive Conformal Inference
Heudiasyc - UMR CNRS 7253, Université de Technologie de Compiègne
Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression
Jun 4, 2026Julia Reuter, Fabricio Olivetti de FrancaUncertainty Quantification Regression Uncertainty Estimation
Center for Mathematics, Computation and Cognition (CMCC), Federal University of ABC, Brazil
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
May 10, 2026Johannes Teutsch, Oleksii Molodchyk, Marion Leibold +2Kernel Regression Uncertainty Quantification
Chair of Automatic Control Engineering, Department of Computer Engineering, Technical University of Munich, 80333 Munich, Germany · Institute of Control Systems, Hamburg University of Technology, 21073 Hamburg, Germany · Department of Electrical and Computer Engineering, National University of Singapore, 117583 Singapore
Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing
May 7, 2026Anh Vu Nguyen, Dino Sejdinovic, Tat-Jun ChinRemote Sensing Edge ML
Australian Institute for Machine Learning (AIML), Adelaide University
ConformaDecompose: Explaining Uncertainty via Calibration Localization
Apr 29, 2026Fatima Rabia Yapicioglu, Meltem Aksoy, Alberto Rigenti +4Uncertainty Quantification Conformal Prediction
Department of Computer Science and Engineering, University of Bologna, Italy · Marketing and Sales, Automobili Lamborghini S.p.A, Sant’Agata Bolognese, Italy · RCT Data Science and Security, Technical University Dortmund, Germany +4
Revisiting Neural Activation Coverage for Uncertainty Estimation
Apr 24, 2026Benedikt Franke, Nils Förster, Frank Köster +3Uncertainty Quantification Regression Uncertainty Estimation
DLR Institute for AI Safety and Security, Ulm - Germany · Ruhr University Bochum, Bochum, Germany
CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression
Mar 6, 2026Luben M. C. Cabezas, Sabina J. Sloman, Bruno M. Resende +3Regression Prediction Intervals Conformal Prediction
Department of Statistics, Federal University of S˜ao Carlos, S˜ao Carlos, Brazil · Institute of Mathematics and Computer Science , University of S˜ao Paulo, S˜ao Carlos, Brazil · Universit´e Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, France +1
Practical Deep Heteroskedastic Regression
Mar 2, 2026Mikkel Jordahn, Jonas Vestergaard Jensen, James Harrison +2Uncertainty Quantification Heteroscedastic Regression
Technical University of Denmark · Google DeepMind
Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
Feb 22, 2026Michele Caprio, Katerina Papagiannouli, Siu Lun Chau +1Random Feature Methods Regression Uncertainty Estimation
The University of Manchester, UK · University of Pisa, Italy · Nanyang Technological University, Singapore +1
CLAPS: Aleatoric-Epistemic Scaling via Last-Layer Laplace for Conformal Regression
Dec 1, 2025Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool +1Uncertainty Quantification Conformal Prediction
Department of Computer Engineering, Gachon University
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
Oct 29, 2025Christopher Bülte, Yusuf Sale, Gitta Kutyniok +1Uncertainty Quantification Aleatoric Uncertainty
LMU Munich · 2Munich Center for Machine Learning (MCML) · 3DLR-German Aerospace Center +2
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Jul 21, 2025Gábor Lugosi, Marcos MatabuenaConformal Prediction k-Nearest Neighbors
Department of Economics and Business, Pompeu Fabra University, Barcelona, Spain · ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain · Barcelona Graduate School of Economics +3
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Jul 10, 2025Ilia Azizi, Juraj Bodik, Jakob Heiss +1Uncertainty Quantification Aleatoric Uncertainty
Department of Operations, HEC, University of Lausanne, Switzerland · BegooAI, Switzerland · Department of Statistics, University of California, Berkeley, USA +1
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
Apr 25, 2025Christopher Bülte, Yusuf Sale, Timo Löhr +3Uncertainty Quantification Aleatoric Uncertainty
1LMU Munich · 2Munich Center for Machine Learning (MCML) · 3DLR-German Aerospace Center +2

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Practical Non-Stationary Graph Gaussian Processes

K. S. Sesh Kumar, Viacheslav Borovitskiy

Brevan Howard Centre for Financial Analysis, Imperial Business School · School of Informatics, University of Edinburgh

Gaussian Processes Regression Uncertainty Estimation

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Gaussian processes on graphs provide a principled framework for prediction and uncertainty estimation, but modelling heterogeneous networks often requires balancing flexibility against parameter complexity. Variability at a node may reflect useful signal that should propagate through the network, unreliable measurements whose influence should be suppressed, or a mixture of both. We introduce Variance-Coupled Gaussian Processes (VCGPs), which use a positive auxiliary signal to modulate graph interactions through a single learned coupling exponent. The exponent controls how strongly high-variability nodes propagate or attenuate information, while a cluster-wise extension accommodates different regimes within the same graph. Under stated structural assumptions, we characterize the unique stationary-rescaling point of the basic family, where correlations match those of a stationary graph kernel while node-dependent variances remain. Experiments on traffic, air-quality and weather datasets show competitive prediction and uncertainty estimation relative to stationary and per-node non-stationary baselines, with the largest gains on networks requiring multiple coupling regimes. Stationary correlations after variance rescaling remain sufficient on some data, and performance depends on the auxiliary signal and model selection.