Journal of Machine Learning Research? (200?)?-? Submitted 6/05; Published?/0? Characterizing the function space for Bayesian kernel models

Citations in the corpus

• Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data (2014, background): “esponse, Ghosal and Roy (2006) proved the posterior consistency of Gaussian process prior for nonparametric binary regression no matter what the mean function of Gaussian process prior is set to, and Pillai et al. (2007) extended the result to Poisson distribution. But the consistency for general ex”
• Computationally Efficient Convolved Multiple Output Gaussian Processes (2011, methodology): “The convolution process is closely related to the Bayesian kernel method (Pillai et al., 2007; Liang et al., 2009) for constructing reproducible kernel Hilbert spaces (RKHS), assigning priors to signed measures and mapping these measures through integral operators.”
• Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models (2007, background): “When x i = 1, it reduces to LSKM regression (Suykens et al., 2002).”
• Convergence Types and Rates in Generic Karhunen-Loève Expansions with Applications to Sample Path Properties (2014, methodology): “prior process is then important for the mathematical analysis, as these spaces determine both the approximation properties of the non-parametric method as well as its statistical properties, see e.g. [52, 74, 73]. In this regard note that one of the strengths of these methods is that they can be con”
• Nonparametric Bayesian Kernel Models (2007, background): “An equivalence between G and H exists for an appropriate choice of the the prior distribution on Γ [10].”