Ens-CGP synthesizes ensemble-based inference with Gaussian processes.
problem Ensemble-based inference and Gaussian process modeling.
method Formulates Ens-CGP as a conditional Gaussian process for ensemble moments.
result Ens-CGP provides a unified probabilistic foundation for Kalman-type methods.
Ensembles dynamic models using random feature approximations.
problem Online scalable Bayesian learning with dynamic models and ensembling.
method Random feature approximations and dynamic models using random walks.
result Better performance with alternative basis expansions like Hilbert space Gaussian processes.
Decentralized Gaussian processes for multi-agent systems.
problem Scalable and flexible learning solutions for multi-agent systems.
method Asymptotically exact decentralized solution to Gaussian processes, with online Bayesian model averaging for hyperparameter selection.
result Asymptotically exact decentralized Gaussian process approximation and online Bayesian model averaging.
EnKF's update is shown to be similar to Matheron's method in Gaussian process regression.
problem Data assimilation in high-dimensional systems.
method Empirical Matheron update applied to EnKF.
result Ensemble Kalman Filter's update is equivalent to an empirical Matheron update.
A new method detects outliers using ensembles of Dirichlet process mixtures.
problem Challenges in unsupervised outlier detection using Dirichlet process mixtures.
method Ensembles of Dirichlet process Gaussian mixtures with random subspace and subsampling.
result Empirically outperforms existing approaches in unsupervised outlier detection.
Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were …
GEnBP combines EnKF and GaBP for efficient high-dimensional inference.
problem Efficient inference in high-dimensional models.
method Gaussian Ensemble Belief Propagation algorithm combining EnKF and GaBP.
result GEnBP outperforms existing methods in accuracy and efficiency.
A new ensemble of Gaussian processes improves active learning efficiency.
problem Efficiently labeling data in high-cost domains like medical imaging.
method Adaptive weighted ensemble of Gaussian processes (EGP) for active learning.
result EGP-based approaches outperform single GP-based active learning methods.
GPR ensemble method predicts stock returns efficiently.
problem Predicting stock returns using machine learning.
method Ensemble Gaussian Process Regression (GPR) for online learning.
result Method outperforms existing models in R R R -squared and Sharpe ratio. A new framework for recycling Gaussian process approximations.
problem Efficiently combining multiple Gaussian process approximations.
method Construct variational ensembles using a dictionary of fitted Gaussian processes.
result Framework allows for various tasks and scalability.
This work evaluates uncertainty in deep Gaussian processes.
problem Uncertainty quantification in deep Gaussian processes.
method Hierarchical deep Gaussian processes (DGPs) and Deep Sigma Point Processes (DSPPs) evaluated on regression and classification tasks.
result DSPPs provide strong in-distribution calibration but are less robust under distribution shift compared to ensembles.
DGMEs use Gaussian mixtures to quantify uncertainty in deep learning.
problem Quantifying uncertainty in complex predictive densities.
method DGMEs use a Gaussian mixture model with an EM algorithm for parameter learning.
result DGMEs outperform state-of-the-art models in uncertainty quantification.
GP-BART improves BART's predictive performance by incorporating Gaussian process priors.
problem Lack of smoothness and explicit covariance structure in BART.
method GP-BART extends BART with Gaussian process priors for tree predictions.
result GP-BART outperforms traditional models in various applications.
The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread. However, the current justification is intuitive rather than analytical. This work proposes one minor modification to the normal ensembling methodology, which we prove allows the ensemble to perform Bayesian inf…
DVE uses GPs on DNN outputs to provide UQ without retraining.
problem Feature collapse in DNNs affects UQ methods.
method Deep Vecchia ensemble (DVE) of GPs on DNN hidden layers.
result Deterministic UQ possible in feature-collapsed DNNs.
A novel approach uses an ensemble of Gaussian processes for robust and adaptive reinforcement learning.
problem Adaptive reinforcement learning in large or continuous state spaces.
method Online scalable (OS) approach with a weighted ensemble of Gaussian processes.
result The ensemble approach improves performance in adversarial settings.
This paper proposes an ensemble of Gaussian processes for Bayesian optimization.
problem Optimizing expensive black-box functions with limited evaluations.
method An ensemble of Gaussian processes (EGP) for adaptive surrogate modeling, combined with Thompson sampling (TS) for function sampling.
result The proposed EGP-TS method achieves better optimization results than single-GP approaches.
BI-EqNO improves Bayesian inference with flexible neural operators.
problem Inaccurate estimation of marginal likelihoods in approximate Bayesian methods.
method Equivariant neural operator framework for generalized approximate Bayesian inference.
result BI-EqNO enhances both deterministic and stochastic approaches to Bayesian inference.
Tree ensemble kernels improve Bayesian optimization for mixed features and constraints.
problem Optimizing over mixed-feature spaces with known constraints.
method Kernel interpretation of tree ensembles as Gaussian Process prior, compatible optimization formulation for acquisition function, integration of known constraints.
result Framework outperforms state-of-the-art methods for mixed-feature spaces and constraints.
NN-GPR improves climate model predictions by preserving fine-scale spatial information.
problem Dilution of fine-scale spatial information and bias in model averaging.
method Gaussian process regression with an infinitely wide deep neural network.
result NN-GPR produces more accurate and detailed climate projections.
Improved neural population modeling using shared features and ensemble detection.
problem Missing shared coding properties in neural latent variable models.
method Feature sharing across tuning curves and soft clustering of neurons.
result More interpretable and better-performing neural population models.
Ensemble Kalman Filter improves GPSSM inference for online learning.
problem Non-mean-field variational inference issues in GPSSM.
method Combining EnKF with NMF variational inference.
result Improved online learning performance and data-fitting accuracy.
Neural network-based post-processing improves ensemble forecast sharpness
problem Reducing the width of central prediction intervals in ensemble forecasts
method Extending loss function with a penalty term
result 8.2%-12.5% reduction in width of central prediction interval
IE-GP framework learns sequentially arriving data with adaptive kernels.
problem Sequential data adaptation in Gaussian process models.
method Incremental ensemble of Gaussian process learners with adaptive weights.
result IE-GP framework effectively learns and adapts to sequential data.
Metrics assess uncertainty structure and distribution for regression models.
problem Quantifying uncertainty in high-dimensional and nonlinear regression tasks.
method Two bounded comparison metrics for uncertainty structure and distribution.
result DNNs and DNOs provide encouraging uncertainty metric values in high dimensions.
Study on Gaussian ensemble of matrix products with mixed moments computed.
problem Understanding the statistical properties of matrix products of Gaussian matrices.
method Analysis of a multi-Wishart ensemble and enumeration of non-crossing pairings.
result Mixed moments of the product matrix are computed and found to be weighted by Fuss-Catalan numbers at large N N N . Multi-task/Multi-output learning seeks to exploit correlation among tasks to enhance performance over learning or solving each task independently. In this paper, we investigate this problem in the context of Gaussian Processes (GPs) and propose a new model which learns a mixture of latent processes by decomposing the c…
There has been a recent surge of interest in modeling neural networks (NNs) as Gaussian processes. In the limit of a NN of infinite width the NN becomes equivalent to a Gaussian process. Here we demonstrate that for an ensemble of large, finite, fully connected networks with a single hidden layer the distribution of ou…
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generat…
SNGP improves DNNs' uncertainty estimation with minimal changes.
problem Uncertainty estimation in deep learning models for real-time applications.
method Formalizing uncertainty as a minimax problem, SNGP adds weight normalization and replaces the output layer with a Gaussian process.
result SNGP outperforms other single-model approaches in uncertainty estimation across vision and language tasks.
This work improves Gaussian process regression for large, non-stationary data.
problem Scalability issues and performance degradation for non-stationary data.
method Combines variational free energy approximations with online expectation propagation and local splitting steps.
result Incremental adaptation to locality, heterogeneity, and non-stationarity in training data.
This work preserves linear invariants in ensemble filters for non-Gaussian data assimilation.
problem Maintaining critical invariants like mass, stoichiometric balance, and charge in non-Gaussian data assimilation.
method Introducing a novel class of nonlinear ensemble filters using measure transport theory.
result Recovery of a constrained Kalman filter for Gaussian settings and combination with regularization techniques.
Bayesian deep ensembles improve prediction accuracy in various settings.
problem Improving prediction accuracy of deep ensembles in out-of-distribution settings.
method Introducing a randomised, untrainable function to each ensemble member, enabling a posterior predictive distribution interpretation.
result Bayesian deep ensembles make more conservative predictions and outperform standard ensembles in various tasks.
A scalable GP model for online uncertainty quantification over graphs.
problem Scalable uncertainty quantification over graphs with dynamic data.
method Graph-aware parametric Gaussian process model using random features and online conformal prediction.
result Improved coverage and efficient prediction sets over existing methods.
This paper uses Bayesian optimization to efficiently identify stochastic dynamical systems.
problem Efficiently identifying linear stochastic dynamical systems with unknown coefficients and noise variances.
method Adaptive Bayesian optimization with ensemble Gaussian processes (EGP) and Kalman filter recursion.
result BO-based estimator achieves RMSE below the Cramer-Rao bound, improving robustness and consistency.
Extends spectral number variance convergence to random matrix ensembles for twisted Laplacians.
problem Spectral number variance convergence for twisted Laplacians and Dirac operators.
method Extends Rudnick's approach to Gaussian ensembles for twisted Laplacians and Dirac operators.
result Convergence to Gaussian ensembles for twisted Laplacians and Dirac operators.
VMGP extends Gaussian processes for Bayesian meta-learning, improving uncertainty prediction.
problem Bayesian meta-learning for few-shot tasks with non-Gaussian uncertainty.
method VMGP (Variational Meta-Gaussian Processes) extends Gaussian processes to model non-Gaussian predictive posteriors.
result VMGP significantly outperforms existing Bayesian meta-learning methods on complex tasks.
ES reduces high-probability regret in stochastic linear bandits.
problem High-probability regret in stochastic linear bandits.
method Linear ensemble sampling with standard Gaussian perturbations, analyzing m = Θ ( d log n ) m=Θ(d\log n) m = Θ ( d log n ) ensemble size. result ES achieves i l d e O ( d 3 / 2 n ) ilde O(d^{3/2}\sqrt n) i l d e O ( d 3/2 n ) high-probability regret, closing the gap to Thompson sampling. A new method combines Gaussian graphical models for better distributed Gaussian process predictions.
problem Poor results from traditional DGP due to violated conditional independence assumption.
method Proposes using Gaussian graphical models to aggregate local predictions from subsets of data.
result Our method outperforms other state-of-the-art DGP approaches on both synthetic and real datasets.
TOO optimizes stochastic epidemiological models by finding both parameter settings and random seeds.
problem Calibrating stochastic epidemiological models to match empirical observations.
method Gaussian process surrogates and Thompson sampling for optimization.
result Produces actual trajectories consistent with ground truth.
Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly…
The study improves solar irradiance forecasts for Chile using machine learning.
problem Accurate short-term PV power forecasts for Chile's Atacama Desert.
method 8-member ensemble forecasts of solar irradiance using WRF model, calibrated with EMOS and DRN.
result Machine learning-based post-processing methods improve forecast accuracy and calibration.
The paper develops a stationary-distribution theory for Random Forest ensemble size selection.
problem Determining the optimal number of trees in Random Forests.
method Modeling the ensemble size as a birth-death Markov chain and deriving its stationary distribution.
result The stationary ensemble size B ∗ B_* B ∗ scales as O ( ε − 2 ) O(\varepsilon^{-2}) O ( ε − 2 ) as ε ↓ 0 \varepsilon\downarrow 0 ε ↓ 0 . Creating maps is an essential task in robotics and provides the basis for effective planning and navigation. In this paper, we learn a compact and continuous implicit surface map of an environment from a stream of range data with known poses. For this, we create and incrementally adjust an ensemble of approximate Gauss…
Better uncertainty estimates for neural networks using Gaussian process priors.
problem Poor uncertainty estimates in neural networks, especially on out-of-distribution data.
method Characterize the function-space prior of an ensemble of infinitely-wide neural networks as a Gaussian process and use it to build a probabilistic model.
result The approach improves calibration of neural networks, especially under distributional shift.
New framework models complex spatial data with basis functions and graphical vectors.
problem Modeling highly-multivariate spatial processes with varying resolutions.
method Extends graphical lasso to multivariate Gaussian processes with independent graphical vectors at different resolutions, using an orthogonal basis and fusion penalty.
result Linear complexity and parsimonious conditional independence structure in multilevel graphical model.
Unified framework for ensemble transport-based smoothing of non-Gaussian time series.
problem Bayesian time series re-analysis with non-Gaussian distributions.
method Measure transport approach to derive consistent prior-to-posterior transformations.
result General ensemble framework for transport-based smoothing of state-space models.
A new method for uncertainty estimation in neural networks using Gaussian-softmax integration.
problem Quantifying uncertainty in neural network predictions.
method Proposes a single-model approach integrating Gaussian distribution with softmax outputs, using mean-field approximation.
result Competitive performance on uncertainty estimation tasks and outperforms many methods on out-of-distribution detection.