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João Paulo Pordeus Gomes

2 accepted papers

2024

Amortized Variational Deep Kernel Learning

ICML 2024poster

Deep kernel learning (DKL) marries the uncertainty quantification of Gaussian processes (GPs) and the representational power of deep neural networks. However, training DKL is challenging and often leads to overfitting. Most notably, DKL often learns “non-local” kernels — incurring spurious correlati…

Cited by 4SourcePDFScholar
2023

Thin and deep Gaussian processes

NeurIPS 2023poster

Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the correlation distance of function values.However, selecting an appropriate kernel can be challenging. Deep GPs avoid man…

Cited by 4SourcePDFScholar