Library · Machine learning & AI
What Gaussian Processes got wrong, from 53 theses
Summary written by a model from the records below: The collected records evaluate Gaussian process models across surrogate modeling, control, time series forecasting, and optimization tasks. While Gaussian processes provide probabilistic predictions, they frequently fail due to cubic computational scaling, poor extrapolation beyond training domains, vulnerability to non-Gaussian noise, and underperformance relative to simpler baselines. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 10 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Cubic computational complexity and memory limits render exact inference intractable on large datasets
Exact Gaussian process inference requires matrix inversions that scale cubically with sample size, creating prohibitive computation times and memory bottlenecks. Practitioners rejected full models or saw them fail during real time control, large flight loads surrogate modeling, and high resolution spatial mapping.
Lost to a baseline
B-spline terrain fitting ran in 32.63s compared to Gaussian Process at 141.99s, though GP yielded lower error
A Framework for Offline Risk-aware Planning of Low-altitude Aerial Flights during Urban Disaster Response · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected exact O(N^3) Gaussian process regression in dynamic pairwise models in favor of linear-time state-space models via Kalman/RTS smoothers
Discrete-Choice Mining of Social Processes · EPFL · PhD
Considered and rejected
Considered and rejected: Rejected covariance/uncertainty prediction during structural optimization in Gaussian Process buckling surrogates due to requirement of per-prediction linear solves; used only precomputed mean dot products.
Data-Driven and GPU-Accelerated Computational Methods for High-Fidelity Aerostructural Design · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected Gaussian Process Bayesian Optimization due to O(n^3) matrix inversion scaling, adopting neural network heteroscedastic surrogate models instead.
Surrogate Modeling for Semiconductor Packaging and Systems Using Machine Learning · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Gaussian Process Regression (GPR) achieved the best RMSE in initial MATLAB tests but was rejected for full block-model estimation because it was computationally intractable for predicting ~6 million block centroids.
A Comparative Study of Machine Learning and Traditional Techniques for Grade Prediction and Grade-Tonnage Evaluation in a Small VMS Deposit · Virginia Tech · Master's
Tried and failed
Gaussian process implicit surfaces with derivative observations applied to 3D surface reconstruction. Outcome: infeasible cost. Reason: Surface normal observations added high computational cost without significant reconstruction accuracy improvement over off-surface points
Advancing the Adaptability of Compliant Robot Controllers for Exploration, Interaction, and Manipulation · EPFL · PhD
Tried and failed
standard full Gaussian process regression applied to large dataset surrogate modeling. Outcome: infeasible cost. Reason: cubic computational complexity and memory limits on large flight loads dataset
Robust optimisation of wing aerostructural response · Cranfield · PhD
Tried and failed
pointwise Gaussian process regression applied to point cloud segmentation. Outcome: too slow. Reason: computational cost was too high for real-time processing and struggled with occlusion
Online vehicle trajectory extraction based on LiDAR data · Texas Tech · PhD
Tried and failed
Gaussian process regression applied to online adaptive control. Outcome: too slow. Reason: computational complexity became intractable with more than 500 data points
Safe Robot Planning and Control Using Uncertainty-Aware Deep Learning · Georgia Tech · PhD
Tried and failed
centered Gaussian process parameterization in Stan applied to transmission flow rate estimation. Outcome: infeasible cost. Reason: computationally intractable inversion of large covariance matrices
Bayesian methods for source attribution using HIV deep sequence data · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected fully nonparametric Gaussian Process feedback kernels in favor of semiparametric delayed-exponential kernels due to massive computational integration costs and numerical instability during forecasting.
Hawkes Processes in Marketing · Penn · PhD
Considered and rejected
Considered and rejected: Rejected continuous spatio-temporal Gaussian Process learning without gridding due to prohibitive computational costs in sizeable urban areas.
Urban air pollution modelling with machine learning using fixed and mobile sensors · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected treating collision as an independent coupled constraint via a parallel Gaussian process model during Bayesian optimization due to increased computational cost and convergence difficulty.
Origami based deployable surfaces: an optimization approach · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected Gaussian Process integral-based traversability evaluation across continuous paths due to high computational cost, opting for upper confidence bound slope sampling inside an ellipse.
Safe Bipedal Locomotion and Navigation in Uncertain Environments · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected full exact Gaussian Process Bayesian inference (O(N^3) scaling) in favor of sparse variational Gaussian processes with M=500 inducing points (O(NM^2) scaling) for computational feasibility.
Computational Modeling of Multi-Agent, Continuous Decision Making in Competitive Contexts · Duke · PhD
Simpler parametric baselines and alternative machine learning models frequently outperform Gaussian processes
Standard and deep Gaussian process models were repeatedly beaten by simpler polynomial regression, ridge regression, random forests, and gradient boosting algorithms. In tasks such as multimodal signal fusion, nonlinear operator approximation, and active learning emulation, Gaussian processes failed to provide accuracy advantages over these baselines.
Tried and failed
Gaussian process regression for multimodal signal fusion applied to locomotion sensor and oscillator fusion. Outcome: worse than baseline. Reason: Yielded no significant performance benefit over simple linear regression
A multi-sensor, hybrid model- and signal-based control system for powered lower limb prostheses · Imperial · PhD
Tried and failed
Gaussian process regression applied to nonlinear sinusoidal degradation signals. Outcome: worse than baseline. Reason: Deviated more from expected range under Bayesian likelihood estimation compared to neural network alternatives
Uncertainty Management in the Prediction of the Remaining Useful Life of Ball Bearings and Li-ion Batteries · Texas Tech · PhD
Tried and failed
Gaussian process regression applied to solving nonlinear partial differential equations. Outcome: worse than baseline. Reason: forced linear approximations limited performance on non-linear operators
Merging First-Principles with Machine Learning for the Optimization of Process and Energy Systems · Georgia Tech · PhD
Tried and failed
Gaussian process regression with polynomial trend term applied to astronomical radial velocity time series. Outcome: no signal. Reason: Did not improve Bayesian log-evidence compared to simpler model
Detection and Characterization of Hot Super-Earth Exoplanets · MIT · PhD
Tried and failed
Gaussian process regression applied to spatial productivity regression. Outcome: worse than baseline. Reason: Severe cross-validation performance with negative R² values compared to random forests
Bringing Gene Drive Modeling Closer to Reality · Cornell · PhD
Tried and failed
multi-task Gaussian processes applied to low-noise multi-output regression. Outcome: worse than baseline. Reason: estimation error in the task covariance matrix when sampling variation is small
Distribution-free statistical process control and Bayesian feasibility determination · Georgia Tech · PhD
Lost to a baseline
Binding score method slightly outperformed Gaussian process regression in RMSE in the least complex linear simulation model (Model 1) under high running variable correlations.
Measurement in K-12 Policy Analysis · Harvard · PhD
Lost to a baseline
Binding score method slightly outperformed Gaussian process regression in precision and RMSE in the least complex linear simulation model (Model 1) under very high running variable correlations.
Measurement in K-12 Policy Analysis · Harvard · PhD
Lost to a baseline
3rd-order polynomial regression beat Gaussian process regression for port transmission variance prediction with LOOCV MSE 1.71e-3 vs 0.0214 across the spectrum.
Statistical Modeling of the Effects of Process Variations on Silicon Photonics · MIT · PhD
Lost to a baseline
Model-based sensor placement methods (conditional Entropy and Mutual Information criteria based on Gaussian Processes) were outperformed by data-driven placement methods (Greedy SVR and PCA with QR-Pivoting) for contact localization accuracy.
Machine-Learning-Driven Haptic Sensor Design · Tübingen · PhD
Considered and rejected
Considered and rejected: Rejected standard Bayesian optimization (BaO) for parameter space exploration because BaO is constrained to Gaussian process regression, which was outperformed by linear ridge regression in modeling brain responses.
An active learning framework for quantifying the effect of neuromodulation · Georgia Tech · PhD
Lost to a baseline
Deep Gaussian Process (DGP) achieved lower coverage than the simpler shallow GP in leave-one-out cross-validation for Florida TC error fields.
Bayesian Uncertainty Quantification while Leveraging Multiple Computer Model Runs · Virginia Tech · Doctorate
Lost to a baseline
On the Langley Glide-Back Booster experiment, both 2- and 3-layer DGPs were unable to match the out-of-sample RMSE of treed Gaussian processes (TGP).
Deep Gaussian Process Surrogates for Computer Experiments · Virginia Tech · Doctorate
Lost to a baseline
Gaussian Process surrogate underperformed GradientBoosting during Bayesian optimization, dipping below p=0.05 around iteration 50.
Data-Driven Design of Recycling-Friendly Aluminium Alloys · MIT · PhD
Tried and failed
deep Gaussian process active learning applied to aerodynamic dynamics emulation. Outcome: worse than baseline. Reason: suboptimal Bayesian MCMC parameter inference hindered performance compared to standard kriging
Active Learning Methods for Emulation and Inverse Design · Georgia Tech · PhD
High dimensional input spaces and multi-output dependencies cause performance to collapse
Scaling Gaussian processes to high dimensional feature spaces or coupled multi-output channels led to prohibitive optimization costs and degraded surrogate accuracy. In higher dimensions, neural network surrogates and evolutionary approaches routinely surpassed Gaussian processes in capturing complex correlations and managing uncertainty.
Tried and failed
Gaussian process as surrogate forward model applied to offline model-based optimization. Outcome: worse than baseline. Reason: underperformed compared to neural network surrogate models on top-1 evaluation tasks
Distributionally Robust Machine Intelligence for Medicine and Scientific Discovery · Penn · PhD
Tried and failed
multi-output Gaussian process with auxiliary information applied to Bayesian optimization. Outcome: worse than baseline. Reason: performed barely better than random sampling while being computationally prohibitive
Lost to a baseline
Radial basis function (RBF) surrogate outperformed Gaussian process regression on the 6-dimensional OTL circuit benchmark function
RETROSPECTIVE AND EXPLORATORY ANALYSES FOR ENHANCING THE SAFETY OF ROTORCRAFT OPERATIONS · Georgia Tech · PhD
Lost to a baseline
Gaussian Process regression was outperformed by Neural Network architectures in higher dimensions (e.g., 20D MMT dataset) in terms of estimated uncertainty.
Tried and failed
two-phase Gaussian process adaptive sampling applied to high-dimensional benchmark function optimization. Outcome: infeasible cost. Reason: prohibitive computational time and memory scaling at n=1500 in 12 dimensions
Active Learning Methods for Emulation and Inverse Design · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Decided against Gaussian Process Regression (GPR) for large output dimensions (fullerene/graphene) due to the computational expense of matrix inversion in high-dimensional spaces.
Transferable Coarse-Grained Models: From Hydrocarbons to Polymers, and Backmapped by Machine Learning · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Multi-output Gaussian Process regression: rejected in favor of three independent scalar GPs due to computational intensity and complex model selection
Data-Driven, Non-Parametric Model Reference Adaptive Control Methods for Autonomous Underwater Vehicles · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Rejected standard Gaussian process regression for Bayesian optimization exploration in HOLMES due to poor scaling in high-dimensional binary search spaces, adopting Genetic Algorithm recombination/mutation instead
Robust Representation Learning and Real-Time Serving of Deep Models for Health Time Series · Georgia Tech · PhD
Tried and failed
Gaussian process active learning applied to high-dimensional state-action correlation modeling. Outcome: worse than baseline. Reason: failed to capture complex multi-dimensional correlations across the task space
Learning and optimization of anticipatory feedback controllers for robot manipulation · EPFL · PhD
Prior misspecification and variational approximations distort uncertainty quantification
Flat priors, oversmoothing assumptions, or fully Bayesian hyperparameter integration produced collapsed credible interval coverage or overly diffuse predictive variances. Similarly, Gaussian approximations of deep Gaussian process posteriors yielded inaccurate variance estimates that corrupted acquisition functions in Bayesian optimization.
Tried and failed
gradient-based hyperparameter optimization for sparse Gaussian processes applied to robotic manipulation dynamic modeling. Outcome: overfit. Reason: optimizers consistently overestimated signal variance, treating true functional features as noise
Decision-Making Architectures for Control of Uncertain Systems · Georgia Tech · PhD
Tried and failed
Gaussian approximation of deep Gaussian process posteriors applied to Bayesian optimization uncertainty estimation. Reason: yielded poor uncertainty quantification and inaccurate variance estimates for acquisition functions
Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing · Virginia Tech · Doctorate
Tried and failed
Gaussian process regression with polynomial kernel applied to uncertainty quantification and predictive modeling. Outcome: did not generalise. Reason: Severely overestimated predictive standard deviation, producing overly wide and uninformative confidence intervals
Engineering-driven Machine Learning Methods for System Intelligence · Virginia Tech · Doctorate
Tried and failed
Gaussian process regression with oversmoothing prior applied to nonparametric regression uncertainty quantification. Reason: Prior smoothness exceeding true function regularity causes asymptotic frequentist credible interval coverage to collapse to zero
Theoretical and methodological advances in Bayesian semiparametrics with variational inference · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected standard Gaussian approximation of DGP posteriors via DSVI because variance estimates were inaccurate for BO acquisition.
Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing · Virginia Tech · Doctorate
Standard Gaussian process assumptions fail under non-Gaussian noise, discrete counts, and outliers
Models assuming constant homoscedastic noise or standard normal distributions struggled when applied to discrete spatial counts, asymmetric heavy tails, and heteroscedastic measurement errors. The presence of leverage points and outliers also biased hyperparameter estimation and degraded cross validation performance.
Tried and failed
Gaussian process regression applied to equipment health degradation modeling. Outcome: did not generalise. Reason: Multivariate normal distributional assumptions do not match operational engineering data
Advanced data-driven methods for prognostics and life extension of assets using condition monitoring and sensor data. · Cranfield · PhD
Tried and failed
standard Gaussian process regression applied to data with leverage points and outliers. Outcome: did not generalise. Reason: mean hyperparameter optimization via weighted least squares causes severe bias from vertical outliers and bad leverage points
Robust and Data-Driven Uncertainty Quantification Methods as Real-Time Decision Support in Data-Driven Models · Virginia Tech · Doctorate
Tried and failed
homoscedastic Gaussian process regression applied to noisy measurement interpolation. Outcome: worse than baseline. Reason: Arbitrary constant noise assumption failed to capture spatially varying observational uncertainty.
Bayesian Machine Learning Algorithms for Uncertainty Quantification, Optimization, and Equation Discoveries in Engineering Physics · Cornell · PhD
Considered and rejected
Considered and rejected: Homoscedastic Gaussian process regression with globally constant noise variance; rejected because it failed to capture spatially and temporally varying intrinsic scatter in edge plasma measurements.
Tried and failed
Gaussian process regression applied to sparse discrete spatial count data. Outcome: worse than baseline. Reason: failed to model spatial distribution of sparse discrete counts effectively
Adaptive Robotic Search and Sampling of Sparse Natural Phenomena · MIT · PhD
Lost to a baseline
Integrated MBO (Bayesian Optimization with standard Gaussian Process targeting mean performance) performed worse than decoupled MNN and MTree methods on asymmetric, heavy-tailed performance distributions.
Machine Learning for Efficient and Robust Datacenter Performance Management · Duke · PhD
Stationary kernels suffer severe extrapolation errors and revert to the prior outside the training domain
When tasked with long range time series forecasting, frequency spectra prediction, or out of domain surrogate evaluation, stationary kernels rapidly decayed toward the prior mean. This lack of extrapolation capability caused massive prediction errors and led researchers to reject Gaussian processes for bounded growth curves.
Tried and failed
Gaussian process regression for adsorption property prediction applied to flexible porous material gas loading. Outcome: did not generalise. Reason: linear and kernel regression could not capture complex nonlinear coupling and extrapolate loading ratios accurately
Efficient and Accurate Incorporation of Flexibility and Defects Into the Modeling of Adsorption in Metal-Organic Frameworks · Georgia Tech · PhD
Tried and failed
precision-weighted aggregation of local gaussian processes applied to spatial satellite temperature prediction. Outcome: did not generalise. Reason: aggregation degraded predictions across large unobserved spatial extrapolation regions
Precision Aggregated Local Models · Virginia Tech · Doctorate
Tried and failed
tree-based Gaussian process regression applied to extrapolation of performance distributions. Outcome: did not generalise. Reason: generated massive prediction errors during extrapolation beyond the training domain
Statistical Methods for Variability Management in High-Performance Computing · Virginia Tech · Doctorate
Tried and failed
Gaussian process regression with RBF kernel applied to long-range time series extrapolation. Outcome: did not generalise. Reason: Stationary RBF kernels revert to prior mean when extrapolating beyond characteristic lengthscale.
Machine learning-based aging models for estimating battery state of health and predicting future degradation · Iowa State · PhD
Tried and failed
Gaussian processes with RBF kernels applied to extrapolating oscillatory frequency spectra. Outcome: did not generalise. Reason: Rapid correlation decay causes predictions to quickly revert to the prior outside the training range.
Rapid Assessment and Uncertainty Quantification for Electrical Responses Using Machine Learning Based Predictions and Extrapolations · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected using Gaussian Processes directly to map feedstock characteristics to BMP biogas production curves because GP lacks extrapolation capability outside training data bounds.
Facilitating the predictions of batch and continuous anaerobic digestion processes performance with statistical tools · Cranfield · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Learn robot motion model uncertainty from data using Gaussian processes as priors within the state estimation factor graph. Blocker: None
Methods for Vision-Based State Estimation and Online Motion Model Adaptation Using Multi-Modal Measurements and Motion Constraints · Tübingen · PhD
Left open
Implement online Gaussian Process Regression hyperparameter retraining at runtime and measure allocation delay and robot distribution in simulation. Blocker: None
MACROSCOPIC ENSEMBLE METHODS FOR MULTI ROBOT TASK ASSIGNMENT IN DYNAMIC ENVIRONMENTS · Penn · PhD
Left open
Implement Gaussian Process Regression instead of uniform RBF grid placement to model non-linear surface force corrections in dynamical system modulation. Blocker: None
Control of Contact Tasks in Autonomous and Human-Robot Collaborative Scenarios: A Dynamical System Approach · EPFL · PhD
Left open
Implement online Gaussian Process regression to learn environmental disturbances concurrently during multi-robot task allocation and RCBF-based control execution. Blocker: None
A Data-Driven Approach to Long-Duration Autonomy for Heterogenous Robot Teams · Georgia Tech · PhD
Left open
Extend static sensor allocation, reward functions, and motion planning to continuous space using Gaussian process regression and interpolation. Blocker: None
Hybrid Sensor Networks for Active Monitoring: Collaboration, Optimization, And Resilience · Georgia Tech · PhD
Left open
Replace the nonstationary Gaussian process regression component with a deep Gaussian process regression model for RUL prediction and uncertainty quantification. Blocker: None
NOVEL ARCHITECTURE AND MACHINE LEARNING MODELS FOR SYSTEM PROGNOSTIC AND REMAINING USEFUL LIFE PREDICTION WITH UNCERTAINTY QUANTIFICATION · Georgia Tech · PhD
Left open
Benchmark and compare different Gaussian process regression kernels and models for modeling stellar activity in radial velocity data of young stars. Blocker: None
The epoch of giant planet migration : searching for young planets within the stellar noise · UT Austin · PhD
Left open
Implement and compare Gaussian process regression and Bayesian calibration for uncertainty quantification in the Wire Arc DED PINN framework. Blocker: None
Improving Part Quality in Wire Arc DED through Machining-Based Hybrid Manufacturing and Scientific Machine Learning · Georgia Tech · PhD
Left open
Replace Gaussian mixture modeling of potential outcome residuals with non-parametric clustering algorithms to estimate complier average treatment effects under measurement error. Blocker: None
Causal Inference with Measurement Errors: with Applications to Experimental and Observational Studies · MIT · PhD
Left open
Implement online local approximate Gaussian processes using mean-square prediction error design criteria instead of k-nearest neighbors for neighborhood selection. Blocker: None
Multi-Stage Modeling with Gaussian Processes · Virginia Tech · Doctorate
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Lily An · Harvard · PhD · 2025
Measurement in K-12 Policy Analysis - Shangkun Wang · Georgia Tech · PhD · 2025
Active Learning Methods for Emulation and Inverse Design - Raghav Gnanasambandam · Virginia Tech · Doctorate · 2024
Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing - Sean Phillip Engelstad · Georgia Tech · PhD · 2026
Data-Driven and GPU-Accelerated Computational Methods for High-Fidelity Aerostructural Design - Yiliang Guo · Georgia Tech · PhD · 2026
Rapid Assessment and Uncertainty Quantification for Electrical Responses Using Machine Learning Based Predictions and Extrapolations - Luca Francesco Montanelli · MIT · PhD · 2026
Data-Driven Design of Recycling-Friendly Aluminium Alloys - Anna Rabren Flowers · Virginia Tech · Doctorate · 2026
Multi-Stage Modeling with Gaussian Processes - Samuel Champer · Cornell · PhD · 2025
Bringing Gene Drive Modeling Closer to Reality - Asif Rashid · Georgia Tech · PhD · 2025
Improving Part Quality in Wire Arc DED through Machining-Based Hybrid Manufacturing and Scientific Machine Learning - Oluwaseyi Ife Akinwande · Georgia Tech · PhD · 2025
Surrogate Modeling for Semiconductor Packaging and Systems Using Machine Learning - Cemile Dilara Bag · Virginia Tech · Master's · 2025
A Comparative Study of Machine Learning and Traditional Techniques for Grade Prediction and Grade-Tonnage Evaluation in a Small VMS Deposit - Henrique Laurino dos Santos · Penn · PhD · 2025
Hawkes Processes in Marketing
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