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

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Showing 476–500 of 1963 papers

TitleStatusHype
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