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Found 16 papers, 5 papers with code
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics
By differentiating treatment components, incorporating their dosages, and sharing statistical information across patients via a hierarchical multi-output Gaussian process, our method improves prediction accuracy over existing approaches, and allows us to interpret the different effects of carbohydrates and fat on the overall glucose response.
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets.
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods gets entangled with the learning of the model hyperparameters.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds
Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters.
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning.
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes
Our model enables the estimation of a treatment policy from observational sequences of treatments and outcomes, and it can predict the interventional and counterfactual progression of the outcome after an intervention on the treatment policy (in contrast with the causal effect of a single treatment).
Non-separable Spatio-temporal Graph Kernels via SPDEs
Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs.
GPflux: A Library for Deep Gaussian Processes
GPflux is compatible with and built on top of the Keras deep learning eco-system.
A Tutorial on Sparse Gaussian Processes and Variational Inference
In this context, a convenient choice for approximate inference is variational inference (VI), where the problem of Bayesian inference is cast as an optimization problem -- namely, to maximize a lower bound of the log marginal likelihood.
Amortized variance reduction for doubly stochastic objectives
Approximate inference in complex probabilistic models such as deep Gaussian processes requires the optimisation of doubly stochastic objective functions.
A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for small variations in the model or inference.
Variational Gaussian Process Models without Matrix Inverses
We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
We introduce a novel finite approximation of GP-modulated Cox processes where positiveness conditions can be imposed directly on the GP, with no restrictions on the covariance function.
Scalable GAM using sparse variational Gaussian processes
Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linear models.
Learning Invariances using the Marginal Likelihood
Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space.
