Barely Biased Learning for Gaussian Process Regression
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Abstract (arXiv)
Recent work in scalable approximate Gaussian process regression has discussed a bias-variance-computation trade-off when estimating the log marginal likelihood. We suggest a method that adaptively selects the amount of computation to use when estimating the log marginal likelihood so that the bias of the objective function is guaranteed to be small. While simple in principle, our current implementation of the method is not competitive computationally with existing approximations.
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References · 8
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
- 2016GPflow: A Gaussian process library using TensorFlowcited by 139
- 2020Convergence of Sparse Variational Inference in Gaussian Processes Regressioncited by 53
- 2018Constant-Time Predictive Distributions for Gaussian Processescited by 48
- 2021Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradientscited by 20
- 2021Bias-Free Scalable Gaussian Processes via Randomized Truncationscited by 15
- 2015Gauss quadrature for matrix inverse forms with applicationscited by 1
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