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Non-Gaussian Gaussian Processes for Few-Shot Regression

NeurIPS 2021 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: PAC-Bayes and decoupled GP learning · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add normalizing-flow mapping to GP posterior (NGGP vs DKT)DKT + Spectral · mixed-noise sines in-rangeNLL: 0.37 -> 0.44Hurt
add flow mapping to GP with NN Linear kernelDKT + NN Linear · mixed-noise sines in-rangeNLL: 0.45 -> 0.17Helped
add flow mapping with Spectral kernelDKT + Spectral · sines out-of-rangeMSE: 0.04 -> 0.03Helped
add flow mapping with Spectral kernelDKT + Spectral · sines out-of-rangeNLL: -0.70 -> -0.80Helped
add flow mapping with Spectral kernelDKT + Spectral · mixed-noise sines out-of-rangeNLL: 1.58 -> 1.35Helped
add flow mapping with Spectral kernelDKT + Spectral · sines in-rangeMSE: 0.02 -> 0.02No clear effect
add flow mapping with RBF kernelDKT + RBF · sines in-rangeMSE: 1.36 -> 1.02Helped
add flow mapping with Spectral kernelDKT + Spectral · Queen Mary head-pose out-of-rangeNLL: 0.00 -> -0.62Helped
add flow mapping with Spectral kernelDKT + Spectral · Queen Mary head-pose in-rangeNLL: 0.03 -> -0.68Helped
add flow mapping with RBF kernelDKT + RBF · Object pose predictionNLL: 1.35 -> 0.22Helped
add flow mapping with RBF kernelDKT + RBF · Object pose predictionMSE: 1.82 -> 1.98Hurt
add flow mapping with RBF kernelDKT + RBF · NASDAQ100 out-of-rangeNLL: 1.049 -> -2.978Helped

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