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03/25/2023
Autoregressive Conditional Neural Processes
Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractiv...
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11/18/2022
Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement
Deploying environmental measurement stations can be a costly and time-co...
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11/04/2022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
The kernel function and its hyperparameters are the central model select...
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05/15/2022
A Note on the Chernoff Bound for Random Variables in the Unit Interval
The Chernoff bound is a well-known tool for obtaining a high probability...
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03/16/2022
Practical Conditional Neural Processes Via Tractable Dependent Predictions
Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-lear...
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03/14/2022
Modelling Non-Smooth Signals with Complex Spectral Structure
The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a ...
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02/23/2022
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Bayesian neural networks (BNNs) combine the expressive power of deep lea...
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06/07/2021
How Tight Can PAC-Bayes be in the Small Data Regime?
In this paper, we investigate the question: Given a small number of data...
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01/10/2021
The Gaussian Neural Process
Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of mode...
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07/02/2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Stationary stochastic processes (SPs) are a key component of many probab...
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11/14/2019
Scalable Exact Inference in Multi-Output Gaussian Processes
Multi-output Gaussian processes (MOGPs) leverage the flexibility and int...
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10/29/2019