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 501–525 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
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—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
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
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
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Benchmark Results

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