Sigma-Point Filters based on Bayesian Quadrature
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Updated
Mar 29, 2018 - Python
Sigma-Point Filters based on Bayesian Quadrature
Adaptive experimental design for maximizing information gain
Programming assignments and final project of stochastic processes course
The Docker container for MGPfact is primarily used for unsupervised manifold learning of single-cell RNA-seq data and can factorize complex cell trajectories into interpretable branching Gaussian processes.
R Package for modeling omega-reliability coefficient from exogenous or latent space using Gaussian Processes or linear models.
Gaussian Process localization with ToF and RSSI
My implementation of several projects for the course "Probabilistic AI" at ETHZ in 2023, including Bayesian Optimization, Gaussian Processes and Reinforcement Learning.
Normal Gaussian Process and Gaussian Process with Poisson Likelihood
Treed Gaussian process algorithm in Python
Bayesian Learning for Control in Multimodal Dynamical Systems | written in Org-mode
Data and code associated with paper "On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions" currently in review.
Resources and extra documentation for the manuscript "A Global Sensitivity-based Identification of Key Factors on Stability of Power Grid with Multi-outfeed HVDC" published in IEEE Latin America Transactions.
Hyper-Parameter Tuning / BayesianOptimization / Gaussian Process / etc.
Highly performant and scalable out-of-the-box gaussian process regression and Bernoulli classification. Built upon GPyTorch, with a familiar sklearn api.
Gaussian-Process Surrogate Optimisation
A complete expected improvement criterion for Gaussian process assisted highly constrained expensive optimization
Incremental Sparse Spectrum Gaussian Process Regression
Code for 'Memory-based dual Gaussian processes for sequential learning' (ICML 2023)
Interactive Gaussian Processes
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