| Variational Bayes for high-dimensional linear regression with sparse priors K Ray, B Szabó Journal of the American Statistical Association 117 (539), 1270-1281, 2022 | 90 | 2022 |
| Bayesian inverse problems with non-conjugate priors K Ray Electronic Journal of Statistics 7, 2516-2549, 2013 | 88 | 2013 |
| Adaptive Bernstein-von Mises theorems in Gaussian white noise K Ray The Annals of Statistics 45 (6), 2511-2536, 2017 | 72 | 2017 |
| Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions R Nickl, K Ray The Annals of Statistics 48 (3), 1383-1408, 2020 | 49 | 2020 |
| Semiparametric Bayesian causal inference K Ray, A van der Vaart Annals of Statistics 48 (5), 2999-3020, 2020 | 33* | 2020 |
| Spike and slab variational Bayes for high dimensional logistic regression K Ray, B Szabo, G Clara Advances in Neural Information Processing Systems 33, 2020 | 24 | 2020 |
| Minimax theory for a class of non-linear statistical inverse problems K Ray, J Schmidt-Hieber Inverse Problems 32 (6), 065003, 2016 | 15 | 2016 |
| Debiased Bayesian inference for average treatment effects K Ray, B Szabó Advances in Neural Information Processing Systems, 11952-11962, 2019 | 14 | 2019 |
| Nonparametric Bayesian inference for reversible multidimensional diffusions M Giordano, K Ray The Annals of Statistics 50 (5), 2872-2898, 2022 | 13 | 2022 |
| The Le Cam distance between density estimation, Poisson processes and Gaussian white noise K Ray, J Schmidt-Hieber Mathematical Statistics and Learning 1 (2), 101-170, 2018 | 12 | 2018 |
| A Bayesian nonparametric approach to log-concave density estimation E Mariucci, K Ray, B Szabó Bernoulli 26 (2), 1070-1097, 2020 | 9 | 2020 |
| A regularity class for the roots of nonnegative functions K Ray, J Schmidt-Hieber Annali di Matematica Pura ed Applicata 196 (6), 2091–2103, 2017 | 8 | 2017 |
| On the Bernstein-von Mises theorem for the Dirichlet process K Ray, A van der Vaart Electronic Journal of Statistics 15 (1), 2224-2246, 2021 | 5 | 2021 |
| On the inability of Gaussian process regression to optimally learn compositional functions M Giordano, K Ray, J Schmidt-Hieber Advances in Neural Information Processing Systems 35, 22341-22353, 2022 | 4 | 2022 |
| Variational Bayes for high-dimensional proportional hazards models with applications within gene expression M Komodromos, EO Aboagye, M Evangelou, S Filippi, K Ray Bioinformatics, 2022 | 4 | 2022 |
| Asymptotic nonequivalence of density estimation and Gaussian white noise for small densities K Ray, J Schmidt-Hieber Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 55 (4 …, 2019 | 4 | 2019 |
| Asymptotic theory for Bayesian nonparametric procedures in inverse problems KM Ray University of Cambridge, 2015 | 4 | 2015 |
| Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior L Travis, K Ray arXiv preprint arXiv:2310.00097, 2023 | 1 | 2023 |
| Semiparametric inference using fractional posteriors A L'Huillier, L Travis, I Castillo, K Ray arXiv preprint arXiv:2301.08158, 2023 | 1 | 2023 |
| Bayesian estimation in a multidimensional diffusion model with high frequency data M Hoffmann, K Ray arXiv preprint arXiv:2211.12267, 2022 | 1 | 2022 |
Botond SzabóAssociate Professor, Bocconi UniversityVerified email at unibocconi.it
