SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–500 of 1963 papers

TitleStatusHype
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Deep Gaussian Processes for Multi-fidelity ModelingCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Approximate Latent Force Model InferenceCode0
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression EstimatorsCode0
Deep Gaussian Processes with Importance-Weighted Variational InferenceCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Nonlinear Inverse Reinforcement Learning with Gaussian ProcessesCode0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Functional Variational Bayesian Neural NetworksCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
On Bayesian Search for the Feasible Space Under Computationally Expensive ConstraintsCode0
Deep learning with differential Gaussian process flowsCode0
On Exact Computation with an Infinitely Wide Neural NetCode0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Deep Multi-fidelity Gaussian ProcessesCode0
Black-box Coreset Variational InferenceCode0
Deep Neural Networks as Gaussian ProcessesCode0
A Bayesian Take on Gaussian Process NetworksCode0
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibilityCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Deep Kernels with Probabilistic Embeddings for Small-Data LearningCode0
Amortized Variational Inference: When and Why?Code0
Federated Causal Inference from Observational DataCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Bias-Free Scalable Gaussian Processes via Randomized TruncationsCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Bayesian Deep Learning on a Quantum ComputerCode0
Adversarial Attacks on Gaussian Process BanditsCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Deep Structured Mixtures of Gaussian ProcessesCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Explainable Learning with Gaussian ProcessesCode0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Beyond Intuition, a Framework for Applying GPs to Real-World DataCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate dataCode0
Principled Preferential Bayesian OptimizationCode0
A conditional one-output likelihood formulation for multitask Gaussian processesCode0
Detecting Misclassification Errors in Neural Networks with a Gaussian Process ModelCode0
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified