Publications

Submitted Papers:

  1. Durante, D., Pozza, F., Szabo, B. (2023) Skewed Bernstein-von Mises theorem and skew-modal approximations. Arxiv preprint.
  2. Nieman, D., Szabo, B.T. & van Zanten, J.H. (2022) Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression. Arxiv Preprint.
  3. Hadji, A., Hesselink, T., Szabo, B.T. (2022) Optimal recovery and uncertainty quantification fordistributed Gaussian process regression. Arxiv preprint.
  4. Franssen, S. & Szabo, B.T. (2022) Uncertainty Quantification for nonparametric regression using Empirical Bayesian neural networks. Arxiv preprint.
  5. Zaman, A. & Szabo, B.T. (2022) Distributed Nonparametric Estimation under Communication Constraints. Arxiv preprint.
  6. Wouter van Loon, Marjolein Fokkema, Botond Szabo, & Mark de Rooij. (2020) View selection in multi-view stacking: Choosing the meta-learner Arxiv preprint.

Published or Accepted Journal Articles

  1. Szabo, B.T., Vuursteen, L. & van Zanten, J.H. (2023+) Optimal high-dimensional and nonparametric distributed testing under communication constraints. To appear in Annals of Statistics.
  2. Szabo, B. T. & van Zanten, J.H. (2022) Distributed function estimation: adaptation using minimal communication. Mathematical Statistics and Learning 5 (3/4): 159-199.
  3. Ray, K. and Szabo, B. T. (2022) Variational Bayes for high-dimensional linear regression with sparse priors. Journal of the American Statistical Association 117 (539): 1270-1281.
  4. Nieman, D., Szabo, B.T. & van Zanten, J.H. (2022) Contraction rates for sparse variational approximations in Gaussian process regression. Journal of Machine Learning Research 23 (205) :1-26.
  5. van Loon, W., de Vos, F., Fokkema, M., Szabo, B., Koini, M., Schmidt, M., de Rooij, M. (2022) Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer’s disease classification. Frontiers in Neuroscience, section Brain Imaging Methods (16). Doi: 10.3389/fnins.2022.830630.
  6. Szabo, B.T., Vuursteen, L. & van Zanten, J.H. (2022) Optimal distributed composite testing in high-dimensional Gaussian models with 1-bit communication. IEEE Transactions on Information Theory 68 (6), 4070-4084.
  7. Hadji, A. and Szabo, B. (2021) Can we trust Bayesian uncertainty quantification from Gaussian process priors with squared exponential covariance kernel? SIAM/ASA Journal of Uncertainty Quantification 9 (1), 185-230.
  8. van Erven, T. and Szabo, B. (2021) Fast Exact Bayesian Inference for Sparse Signals in the Normal Sequence Model.  Bayesian Analysis 16 (3), 933-960.
  9. Szabo, B. T. and van Zanten, J.H. (2020) Adaptive distributed methods under communication constraints. Annals of Statistics 48 (4), 2347-2380
  10. Rousseau, J. and Szabo, B. (2020). Asymptotic frequentist coverage properties of Bayesian credible sets for sieve priors. Annals of Statistics 48 (4), 2155-2179.
  11. van Loon, W., Fokkema, M., Szabo, B., and de Rooij, M. (2020) Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning. Information Fusion 61 (September) 113-123.
  12. Mariucci, E., Ray, K., and Szabo, B. T. (2020) A Bayesian nonparametric approach to log-concave density estimation. Bernoulli 26 (2), 1070-1097.
  13. Castillo, I. & Szabo, B. (2020) Spike and slab empirical Bayes sparse credible sets. Bernoulli 26 (1), 127-158
  14. Ray, K. & Szabo, B. T. & and van der Vaart, A. (2020) Discussion of Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding and Heterogeneous Effects by Hahn, Murray & Carvalho . Bayesian Anal. 15 (2020), 1026-1028.
  15. Szabo, B. T. and van Zanten, J.H. (2019) An asymptotic analysis of distributed nonparametric methods. Journal of Machine Learning Research 20, 1-30.
  16. van der Pas, S., Szabo, B. and van der Vaart, A. (2017). Uncertainty Quantification for the Horseshoe (with Discussion). Bayesian Analysis 12(4): 1221-1274.(or: arXiv)
  17. van der Pas, S., Szabo, B. and van der Vaart, A. (2017). Adaptive posterior contraction rates for the horseshoe. Electronic Journal of Statistics 11(2): 3196-3225.(or: arXiv)
  18. Rousseau, J. and Szabo, B. (2017). Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator. Annals of Statistics 45(2): 833-865.(or: arXiv)
  19. Nickl, R. and Szabo, B. T. (2016). A sharp adaptive confidence ball for self-similar functions. Stochastic Processes and their Applications 126(12): 3913-3934.(or: arXiv)
  20. Knapik, B. T., Szabo, B. T., van der Vaart, A. W., and van Zanten, J. H. (2016). Bayes procedures for adaptive inference in nonparametric inverse problems. Probability Theory and Related Fields 164 (3), 771-813. (or: arXiv)
  21. Szabo, B. T., van der Vaart, A. W., and van Zanten, J. H. (2015). Rejoinder to discussion of “Frequentist coverage of adaptive nonparametric Bayesian credible sets.” Annals of Statistics 43 (4), 1463 – 1470. (or: arXiv )
  22. Szabo, B. T., van der Vaart, A. W., and van Zanten, J. H. (2015). Honest Bayesian confidence sets for the L2-norm. Journal of Statistical Planning and Inference 166, 36–51. (or: arXiv )
  23. Szabo, B. T., van der Vaart, A. W., and van Zanten, J. H. (2015). Frequentist coverage of adaptive nonparametric Bayesian credible sets. Annals of Statistics 43 (4), 1391 – 1428. (or: arXiv )
  24. Szabo, B. T., van der Vaart, A. W., and van Zanten, J. H. (2013). Empirical Bayes scaling of Gaussian priors in the white noise model. Electronic Journal of Statistics, 7, 991–1018.
  25. Turanyi, T., Nagy, T., Zsely, I. Gy., Cserhai, M., Varga, T., Szabo, B. T., Sedyo, I., Kiss, P. T., Zempleni, A., and Curran, H. J. (2012). Determination of rate parameters based on both direct and indirect measurements. International Journal of Chemical Kinetics, 44(5), 284-302.
  26. Varga, L., Szabo, B., Zsely, I. Gy., Zempleni, A., and Turanyi, T. (2011). Numerical investigation of the uncertainty of Arrhenius parameters. Journal of mathematical chemistry, 49(8), 1798-1809.

Refereed conference publications

  1. Ray, K., Szabo B.T. & Clara, G. (2020) Spike and slab variational Bayes for high dimensional logistic regression. Advances in Neural Information Processing Systems (NeurIPS).
  2. Ray, K. and Szabo, B. T. (2019) Debiased Bayesian inference for average treatment effects. Advances in Neural Information Processing Systems (NeurIPS), 11929-11939.
  3. Szabo, B. T. (2014). Confidence sets from empirical Bayes procedures with conditionally Gaussian priors on Sobolev balls. Proceedings of the 18th European Young Statistician Meeting, 113-117.
  4. Berg, J. B. van den, Castro, R. M., Draisma, J., Evers, J. H. M., Hendriks, M., Khimshiashvili, G., Krehel, O., Kryven, I., Mora, K., Szabo, B. T. and Zwiernik, P. W. (2012). Non-imaging optics for LED-lighting. Proceedings of the 84th European Study Group Mathematics with Industry, 70-103.

Book Chapters

  1. Szabo, B. T. (2015). On Bayesian based adaptive confidence sets for linear functionals. Bayesian Statistics from Methods to Models and Applications 91–105.

R packages

  1. Clara, G., Szabo, B.T. and Ray, K. (2020) sparsevb (Variational Bayes for High-dimensional Linear and Logistic Regression).
  2. de Rooij, S., van Erven, T., Szabo, B.T. (2019) SequenceSpikeSlab (Exact Bayesian Model Selection Methods for the Sparse Normal Sequence Model)

PhD Thesis

Master Thesis

Hungarian Students Scholar Circle (OTDK)

  1. Jordan, T., Szabo, B. T.(2011). A korlatossag vizsgalata irany-hossz vegyes grafok eseten. OTDK, Nyiregyhaza.
  2. Szabo, B. T., Turanyi, T., Zsely, I. Gy. (2010) Arrhenius-paramaterek becslese kozvetett es kozvetlen meresek alapjan. OTDK, 2011.

External collection of publications