On the inability of Gaussian process regression to optimally learn compositional functions

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

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Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber


We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure. To this end, we study information-theoretic lower bounds for posterior contraction rates for Gaussian process regression in a continuous regression model. We show that if the true function is a generalized additive function, then the posterior based on any mean-zero Gaussian process can only recover the truth at a rate that is strictly slower than the minimax rate by a factor that is polynomially suboptimal in the sample size $n$.