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07/06/2023
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Gaussian Processes (GPs) offer an attractive method for regression over ...
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06/19/2023
Practical Equivariances via Relational Conditional Neural Processes
Conditional Neural Processes (CNPs) are a class of metalearning models p...
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06/16/2023
Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions
Deciding on an appropriate intervention requires a causal model of a tre...
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06/07/2023
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Approximate inference in Gaussian process (GP) models with non-conjugate...
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03/29/2023
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory d...
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11/11/2022
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds
Gaussian process training decomposes into inference of the (approximate)...
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11/02/2022
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Gaussian processes (GPs) are the main surrogate functions used for seque...
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09/09/2022
Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions
Policy makers need to predict the progression of an outcome before adopt...
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11/16/2021
Non-separable Spatio-temporal Graph Kernels via SPDEs
Gaussian processes (GPs) provide a principled and direct approach for in...
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04/12/2021
GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
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12/27/2020
A Tutorial on Sparse Gaussian Processes and Variational Inference
Gaussian processes (GPs) provide a framework for Bayesian inference that...
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03/09/2020
Amortized variance reduction for doubly stochastic objectives
Approximate inference in complex probabilistic models such as deep Gauss...
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03/02/2020
A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in large-scale probl...
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02/28/2019
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
Gaussian process (GP) modulated Cox processes are widely used to model p...
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12/28/2018
Scalable GAM using sparse variational Gaussian processes
Generalized additive models (GAMs) are a widely used class of models of ...
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08/16/2018
