61 citations · 327 across the 24 of their papers we have counts for
24 papers
Semiparametric Bernstein-von Mises theorems for reversible diffusions
Matteo Giordano, Kolyan Ray
We establish a general semiparametric Bernstein-von Mises theorem for Bayesian nonparametric priors based on continuous observations in a periodic reversible multidimensional diffu…
A variational Bayes approach to inference for low-dimensional parameters in high-dimensional linear regression
Ismaël Castillo, Alice L'Huillier, Kolyan Ray +1
We propose a scalable variational Bayes method for statistical inference for a single or pre-specified low-dimensional subset of the coordinates of a high-dimensional parameter in…
Bayesian Nonparametric Inference in McKean-Vlasov models
Richard Nickl, Grigorios A. Pavliotis, Kolyan Ray
We consider nonparametric statistical inference on a periodic interaction potential from noisy discrete space-time measurements of solutions of the nonlinear McKean-Vla…
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
Luke Travis, Kolyan Ray
We study pointwise estimation and uncertainty quantification for a sparse variational Gaussian process method with eigenvector inducing variables. For a rescaled Brownian motion pr…
Group Spike and Slab Variational Bayes
Michael Komodromos, Marina Evangelou, Sarah Filippi +1
We introduce Group Spike-and-slab Variational Bayes (GSVB), a scalable method for group sparse regression. A fast co-ordinate ascent variational inference (CAVI) algorithm is devel…
Semiparametric inference using fractional posteriors
Alice L'Huillier, Luke Travis, Ismaël Castillo +1
We establish a general Bernstein--von Mises theorem for approximately linear semiparametric functionals of fractional posterior distributions based on nonparametric priors. This is…
Nonparametric Bayesian estimation in a multidimensional diffusion model with high frequency data
Marc Hoffmann, Kolyan Ray
We consider nonparametric Bayesian inference in a multidimensional diffusion model with reflecting boundary conditions based on discrete high-frequency observations. We prove a gen…
Bayesian material flow analysis for systems with multiple levels of disaggregation and high dimensional data
Junyang Wang, Kolyan Ray, Pablo Brito-Parada +6
Material Flow Analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social and economic impacts…
On the inability of Gaussian process regression to optimally learn compositional functions
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…
Variational Bayes for high-dimensional proportional hazards models with applications within gene expression
Michael Komodromos, Eric Aboagye, Marina Evangelou +2
Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often e…
Nonparametric Bayesian inference for reversible multi-dimensional diffusions
Matteo Giordano, Kolyan Ray
We study nonparametric Bayesian models for reversible multi-dimensional diffusions with periodic drift. For continuous observation paths, reversibility is exploited to prove a gene…
Spike and slab variational Bayes for high dimensional logistic regression
Kolyan Ray, Botond Szabo, Gabriel Clara
Variational Bayes (VB) is a popular scalable alternative to Markov chain Monte Carlo for Bayesian inference. We study a mean-field spike and slab VB approximation of widely used Ba…
On the Bernstein-von Mises theorem for the Dirichlet process
Kolyan Ray, Aad van der Vaart
We establish that Laplace transforms of the posterior Dirichlet process converge to those of the limiting Brownian bridge process in a neighbourhood about zero, uniformly over Gliv…
Debiased Bayesian inference for average treatment effects
Kolyan Ray, Botond Szabo
Bayesian approaches have become increasingly popular in causal inference problems due to their conceptual simplicity, excellent performance and in-built uncertainty quantification…
Variational Bayes for high-dimensional linear regression with sparse priors
Kolyan Ray, Botond Szabo
We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression. Under compatibility condi…
Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions
Richard Nickl, Kolyan Ray
The problem of determining a periodic Lipschitz vector field from an observed trajectory of the solution of the multi-dimensional stoch…
Semiparametric Bayesian causal inference
Kolyan Ray, Aad van der Vaart
We develop a semiparametric Bayesian approach for estimating the mean response in a missing data model with binary outcomes and a nonparametrically modelled propensity score. Equiv…
Asymptotic nonequivalence of density estimation and Gaussian white noise for small densities
Kolyan Ray, Johannes Schmidt-Hieber
It is well-known that density estimation on the unit interval is asymptotically equivalent to a Gaussian white noise experiment, provided the densities are sufficiently smooth and…
A Bayesian nonparametric approach to log-concave density estimation
Ester Mariucci, Kolyan Ray, Botond Szabo
The estimation of a log-concave density on is a canonical problem in the area of shape-constrained nonparametric inference. We present a Bayesian nonparametric approac…
The Le Cam distance between density estimation, Poisson processes and Gaussian white noise
Kolyan Ray, Johannes Schmidt-Hieber
It is well-known that density estimation on the unit interval is asymptotically equivalent to a Gaussian white noise experiment, provided the densities have Hölder smoothness large…
Minimax theory for a class of non-linear statistical inverse problems
Kolyan Ray, Johannes Schmidt-Hieber
We study a class of statistical inverse problems with non-linear pointwise operators motivated by concrete statistical applications. A two-step procedure is proposed, where the fir…
A regularity class for the roots of non-negative functions
Kolyan Ray, Johannes Schmidt-Hieber
We investigate the regularity of the positive roots of a non-negative function of one-variable. A modified Hölder space is introduced such that if $f\in \mathcal{F}…
Adaptive Bernstein-von Mises theorems in Gaussian white noise
Kolyan Ray
We investigate Bernstein-von Mises theorems for adaptive nonparametric Bayesian procedures in the canonical Gaussian white noise model. We consider both a Hilbert space and multisc…
Bayesian inverse problems with non-conjugate priors
Kolyan Ray
We investigate the frequentist posterior contraction rate of nonparametric Bayesian procedures in linear inverse problems in both the mildly and severely ill-posed cases. A theorem…