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06/03/2023
Variational Gaussian Process Diffusion Processes
Diffusion processes are a class of stochastic differential equations (SD...
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11/05/2021
Dual Parameterization of Sparse Variational Gaussian Processes
Sparse variational Gaussian process (SVGP) methods are a common choice f...
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03/26/2021
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
In the past decade, model-free reinforcement learning (RL) has provided ...
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03/19/2021
Sparse Algorithms for Markovian Gaussian Processes
Approximate Bayesian inference methods that scale to very large datasets...
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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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02/03/2020
Non-linear regression models for behavioral and neural data analysis
Regression models are popular tools in empirical sciences to infer the i...
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01/15/2020
Doubly Sparse Variational Gaussian Processes
The use of Gaussian process models is typically limited to datasets with...
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06/21/2019
Disentangled Skill Embeddings for Reinforcement Learning
We propose a novel framework for multi-task reinforcement learning (MTRL...
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02/26/2019
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
Banded matrices can be used as precision matrices in several models incl...
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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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05/28/2018
Discrete flow posteriors for variational inference in discrete dynamical systems
Each training step for a variational autoencoder (VAE) requires us to sa...
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11/03/2017