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Gaussian Processes
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/awav/conjugate-gradient-sparse-gp
공식 코드Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
https://github.com/bahramjafrasteh/missinggps
공식 코드https://github.com/baikeshe/gpr_epi_modeling
공식 코드Modeling Epidemic Spread: A Gaussian Process Regression Approach
https://github.com/baosws/drbo
공식 코드https://github.com/barahona-research-group/BayesFactorSimilarity
공식 코드Similarity measure for sparse time course data based on Gaussian processes
https://github.com/bee-hive/MedGP
공식 코드Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
https://github.com/befelix/SafeMDP
공식 코드Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
https://github.com/belakaria/al-gsa-dgsms
공식 코드Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
https://github.com/benavoli/SkewGP
공식 코드https://github.com/benavoli/choicegp
공식 코드https://github.com/benavoli/prefgp
공식 코드A tutorial on learning from preferences and choices with Gaussian Processes
https://github.com/bessagroup/mfbml
공식 코드Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models
https://github.com/bilkent-cyborg/oclok-ucb
공식 코드Contextual Combinatorial Bandits with Changing Action Sets via Gaussian Processes
https://github.com/billzxl/dil-gp
공식 코드Domain Invariant Learning for Gaussian Processes and Bayesian Exploration
https://github.com/bkiani/Adversarial-robustness-guarantees-for-random-deep-neural-networks
공식 코드Adversarial Robustness Guarantees for Random Deep Neural Networks
https://github.com/bobby-he/bayesian-ntk
공식 코드https://github.com/boschresearch/MetaBO
공식 코드Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
https://github.com/boschresearch/PAC_GP
공식 코드Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
https://github.com/boschresearch/Structured_DGP
공식 코드Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
https://github.com/boschresearch/amor-struct-gp
공식 코드Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels
https://github.com/boschresearch/bosot
공식 코드Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
https://github.com/boschresearch/hierarchical-hyperplane-kernels
공식 코드https://github.com/boschresearch/information-theoretic-safe-exploration
공식 코드Information-Theoretic Safe Exploration with Gaussian Processes
https://github.com/boschresearch/salmogp
공식 코드https://github.com/boschresearch/transfergpbo
공식 코드Transfer Learning with Gaussian Processes for Bayesian Optimization
https://github.com/boschresearch/transfersafesequentiallearning
공식 코드Global Safe Sequential Learning via Efficient Knowledge Transfer
https://github.com/bostanabad-research-group/gp-for-operator-learning
공식 코드https://github.com/bostanabad-research-group/gp-plus
공식 코드GP+: A Python Library for Kernel-based learning via Gaussian Processes
https://github.com/brain-iith/SOD_DGP
공식 코드Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression
https://github.com/brain-research/nngp
공식 코드https://github.com/buseskorkmaz/arteo
공식 코드Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm
https://github.com/cagalvisf/quantum_hsgpq
공식 코드Provable Quantum Algorithm Advantage for Gaussian Process Quadrature
https://github.com/cagatayyildiz/npde
공식 코드Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
https://github.com/cagatayyildiz/npode
공식 코드https://github.com/cagrell/gp_constr
공식 코드Gaussian processes with linear operator inequality constraints
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