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 501–550 of 1963 papers

TitleStatusHype
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
Differentially Private Gaussian Processes—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Differentiating the multipoint Expected Improvement for optimal batch design—0
Bayesian Kernel Shaping for Learning Control—0
Graph Based Gaussian Processes on Restricted Domains—0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Collaborative Gaussian Processes for Preference Learning—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
A Provable Approach for End-to-End Safe Reinforcement Learning—0
A flexible state space model for learning nonlinear dynamical systems—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
A probabilistic Taylor expansion with Gaussian processes—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
A probabilistic data-driven model for planar pushing—0
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery—0
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality—0
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
A Dynamic Programming Algorithm for Finding an Optimal Sequence of Informative Measurements—0
Estimation of Riemannian distances between covariance operators and Gaussian processes—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes—0
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice—0
Towards Practical Lipschitz Bandits—0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Approximation errors of online sparsification criteria—0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
Environmental Modeling Framework using Stacked Gaussian Processes—0
Approximation-Aware Bayesian Optimization—0
Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes—0
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Entropy of Overcomplete Kernel Dictionaries—0
Epidemiological Model Calibration via Graybox Bayesian Optimization—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models—0
Building 3D Generative Models from Minimal Data—0
Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes—0
Adversarially Robust Optimization with Gaussian Processes—0
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Benchmark Results

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