Fetching the paper…
Reading the bibliography…
We study posterior contraction rates for a class of deep Gaussian process priors applied to the nonparametric regression problem under a general composition assumption on the regression function.
Probabilistic construction of the solution of some higher order parabolic differential equation
Funaki, T · 1979
Earlier work this paper cites.
Some path properties of iterated Brownian motion
Burdzy, K · 1993
Earlier work this paper cites.
Composition of stochastic processes governed by higher-order parabolic and hyperbolic equations
Hochberg, K. J., and Orsingher, E · 1996
Earlier work this paper cites.
Bayesian learning for neural networks
Neal, R. M · 1996
Earlier work this paper cites.
Weak convergence and empirical processes
van der Vaart, A. W., and Wellner, J. A · 1996
Earlier work this paper cites.
Convergence rates of posterior distributions
Ghosal, S., Ghosh, J. K., and van der Vaart, A. W · 2000
Earlier work this paper cites.
Gaussian processes: inequalities, small ball probabilities and applications
Li, W. V., and Shao, Q.-M · 2001
Earlier work this paper cites.
Concentration inequalities and model selection
Massart, P · 2003
Earlier work this paper cites.
Random fields and geometry
Adler, R. J., and Taylor, J. E · 2007
Earlier work this paper cites.
Lower bounds for posterior rates with Gaussian process priors
Castillo, I · 2008
Earlier work this paper cites.
Rates of contraction of posterior distributions based on Gaussian process priors
van der Vaart, A. W., and van Zanten, J. H · 2008
Earlier work this paper cites.
Reproducing kernel Hilbert spaces of Gaussian priors
van der Vaart, A. W., and van Zanten, J. H · 2008
Earlier work this paper cites.
Variational learning of inducing variables in sparse gaussian processes
Titsias, M · 2009
Earlier work this paper cites.
Adaptive Bayesian estimation using a Gaussian random field with inverse Gamma bandwidth
van der Vaart, A. W., and van Zanten, H · 2009
Cited alongside, same era.
On the small deviation problem for some iterated processes
Aurzada, F., and Lifshits, M · 2010
Cited alongside, same era.
Quantitative concentration inequalities on sample path space for mean field interaction
Bolley, F · 2010
Cited alongside, same era.
Bayesian learning via Stochastic Gradient Langevin Dynamics
Welling, M., and Teh, Y. W · 2011
Cited alongside, same era.
Fractional fields and applications
Cohen, S., and Istas, J · 2013
Cited alongside, same era.
Deep Gaussian processes
Damianou, A., and Lawrence, N · 2013
Cited alongside, same era.
Fundamentals of nonparametric Bayesian inference
Ghosal, S., and van der Vaart, A · 2017
Later among the works it cites.
Gaussian process emulators for computer experiments with inequality constraints
Maatouk, H., and Bay, X · 2017
Later among the works it cites.
Gaussian process behaviour in wide deep neural networks
Matthews, A., Rowland, M., Hron, J., Turner, R. E., and Ghahramani, Z · 2018
Later among the works it cites.
Posterior concentration for sparse deep learning
Polson, N. G., and Ročková, V · 2018
Later among the works it cites.
On the impact of the activation function on deep neural networks training
Hayou, S., Doucet, A., and Rousseau, J · 2019
Later among the works it cites.
Gaussian Process Modelling under Inequality Constraints
Lopez Lopera, A. F · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Iterating Brownian motions, ad libitum
Curien, N., and Konstantopoulos, T · 2014
Cited alongside, same era.
Processes iterated ad libitum
Casse, J., and Marckert, J.-F · 2016
Cited alongside, same era.
Mathematical foundations of infinite-dimensional statistical models
Giné, E., and Nickl, R · 2016
Cited alongside, same era.
Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Cited alongside, same era.
Small ball probabilities for a class of time-changed self-similar processes
Kobayashi, K · 2016
Cited alongside, same era.
On the asymptotics of supremum distribution for some iterated processes
Arendarczyk, M · 2017
Cited alongside, same era.
Later among the works it cites.
Infinitely deep neural networks as diffusion processes
Peluchetti, S., and Favaro, S · 2020
Later among the works it cites.
Nonnegativity-enforced Gaussian process regression
Pensoneault, A., Yang, X., and Zhu, X · 2020
Later among the works it cites.
Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors
Ray, P., Pati, D., and Bhattacharya, A · 2020
Later among the works it cites.
Nonparametric regression using deep neural networks with ReLU activation function
Schmidt-Hieber, J · 2020
Later among the works it cites.
A survey of constrained gaussian process regression: Approaches and implementation challenges
Swiler, L. P., Gulian, M., Frankel, A. L., Safta, C., and Jakeman, J. D · 2020
Later among the works it cites.
Contraction rates for sparse variational approximations in Gaussian process regression
Nieman, D., Szabo, B., and van Zanten, H · 2021
Closest in time.
On the inability of Gaussian process regression to optimally learn compositional functions
Giordano, M., Ray, K., and Schmidt-Hieber, J · 2022
Closest in time.