Welcome to my personal website!
I am Professor of Econometrics and Statistics at the University of Chicago Booth School of Business.
My research brings together statistics and machine learning to develop tools for learning from large datasets.
I specialize in topics at the intersection of Bayesian and frequentist
statistics,
including:
variable
selection, uncertainty quantification, Bayesian nonparametrics, factor and dynamic models, and high-dimensional decision theory and inference.
Contact Information
Veronika.Rockova@ChicagoBooth.edu369 Charles M. Harper Center
5807 South Woodlawn Avenue
Chicago, IL 60637
Publications and Manuscripts
NEW!
-
Deep Generative Quantile Bayes
Kim, J., Zhai, P. and Rockova V. (2024)
Submitted link -
Adaptive Uncertainty Quantification for Generative AI
O'Hagan S., Kim, J. and Rockova V. (2024)
Submitted link -
Tree Bandits for Generative Bayes
O'Hagan S., Kim, J. and Rockova V. (2024)
Submitted link -
Deep Bayes Factors
Kim, J. and Rockova V. (2023)
Submitted link - Sparse
Bayesian
Multidimensional
Item
Response
Theory
Li, J., Gibbons, R. and Rockova V. (2023)
Journal of the American Statistical Association (Revision Submitted) link -
Adaptive Bayesian Predictive Inference in
High-dimensional Regression
Rockova V. (2023)
Submitted link -
On Mixing Rates for Bayesian CART
Kim, J. and Rockova V. (2023)
Submitted link -
Adversarial Bayesian Simulation
Wang, Y. and Rockova V. (2022)
Journal of Machine Learning Research (Revision Invited) link
Statistics and Machine Learning
- The
Art of BART: On Flexibility of Bayesian Forests
Jeong S. and Rockova V. (2022)
Journal of Machine Learning Research (In press) link -
Ideal Bayesian Spatial Adaptation
Rockova V. and Rousseau J. (2023)
Journal of the American Statistical Association (In Press) link Supplement -
Deep Bootstrap for Bayesian Inference
Nie, L. and Rockova V. (2023)
Philosophical Transactions of the Royal Society A (In Press) link Supplement -
Approximate Bayesian Computation via Classification
Wang, Y., Kaji T. and Rockova V. (2022)
Journal of Machine Learning Research (In Press) link -
Metropolis-Hastings via Classification
Kaji T. and Rockova V. (2022)
Journal of the American Statistical Association, Theory and Methods (In Press) link - The
Bayesian Bootstrap Spike-and-Slab LASSO
Nie L. and Rockova V. (2020)
Journal of the American Statistical Association, Theory and Methods (In Press) link - Uncertainty Quantification for Bayesian CART
Castillo I. and Rockova V. (2021)
The Annals of Statistics (In Press) link - Adaptive Bayesian SLOPE: Model Selection with Incomplete Data
Jiang W., Bogdan M., Josse J., Majewski S., Miasojedow, B., Rockova V. and TraumaBase Group (2021)
Journal of Computational and Graphical Statistics (In Press) link - Variable
Selection via Thompson Sampling
Liu Y. and Rockova V. (2021)
Winner of SBSS 2020 Student Paper Competition awarded by ASA
Journal of the American Statistical Association, Theory and Methods (In Press) link - ABC Variable Selection
with Bayesian Forests
Liu Y., Rockova V. and Wang Y. (2021)
Journal of the Royal Statistical Society, Series B (In Press) pdf - The Median Probability Model and
Correlated Variables
Barbieri M., Berger J., George E. and Rockova V. (2020)
Bayesian Analysis (In Press) link -
Regularization via Bayesian Penalty Mixing
Comment on: Ridge Regularization: An Essential Concept in Data Science by Trevor Hastie
George E. and Rockova V. (2020)
Technometrics (62), 438-442 link -
Spike-and-Slab Meets the LASSO: A Review of the Spike-and-Slab LASSO
Bai R., George E. and Rockova V. (2020)
Handbook on Bayesian Variable Selection (In Press) link -
Determinantal Priors for Bayesian Variable Selection
Rockova V. and George, E. (2020)
Statistics in the Public Interest - In Memory of Stephen E. Feinberg (In Press) link - Spike-and-Slab LASSO Biclustering
Moran G., Rockova V. and George E. (2020)
The Annals of Applied Statistics (15), 148-173 link - On Semi-parametric
Inference for BART
Rockova V. (2020)
37th International Conference on Machine Learning (119), 8137–8146 pdf - Uncertainty Quantification for
Sparse Deep Learning
Wang Y. and Rockova V. (2020)
23rd Conference on Artificial Intelligence and Statistics (108), 298–308 pdf - Dynamic Variable Selection with
Spike-and-Slab Process Priors
Rockova V. and McAlinn K. (2020)
Bayesian Analysis (16), 233-269 pdf - Posterior Concentration for Bayesian
Regression Trees and Forests
Rockova V. and van der Pas S. (2020)
The Annals of Statistics (48), 2108-2131 pdf | Supplement - On Theory for BART
Rockova V. and Saha E. (2019)
22nd Conference on Artificial Intelligence and Statistics (89), 2839–2848 pdf - Posterior Concentration
for Sparse Deep Learning
Polson N. and Rockova V. (2018)
32nd Annual Conference on Neural Information Processing Systems (NeurIPS) pdf - Simultaneous Variable and Covariance Selection with the Multivariate Spike-and-Slab Lasso
Deshpande S., Rockova V. and George E. (2019)
Journal of Computational and Graphical Statistics (18), 921–931 link - On Variance Estimation for Bayesian
Variable Selection
Moran G., Rockova V. and George E. (2019)
Bayesian Analysis (14), 1091–1119
pdf | Supplement - Particle EM for Variable Selection
Rockova V. (2018)
Journal of the American Statistical Association, Theory and Methods (113), 1684-1697
pdf | supplement - The Spike-and-Slab LASSO
Rockova V. and George E. (2018)
Journal of the American Statistical Association, Theory and Methods (113), 431-444
pdf | supplement - Bayesian Estimation of Sparse Signals with a Continuous Spike-and-Slab Prior
Rockova V. (2018)
The Annals of Statistics (46), 401-437
pdf | supplement - Bayesian Dyadic Trees and Histograms for
Regression
van der Pas S. and Rockova V. (2017)
31st Annual Conference on Neural Information Processing Systems (NeurIPS) pdf - Hospital Mortality Rate Estimation for Public Reporting
George E., Rockova V., Rosenbaum, P., Satopaa, V., Silber, J. (2017)
Journal of the American Statistical Association, Applications (112), 933-947 link - Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity
Rockova V. and George E. (2016)
Journal of the American Statistical Association, Theory and Methods (111), 1608-1622
pdf | Supplement - Determinantal Regularization for Ensemble Variable Selection
Rockova V., Moran, G. and George E. (2016)
19th International Conference on Artificial Inteligence & Statistics pdf - Bayesian Penalty Mixing: The Case of a Non-separable Penalty
Rockova V. and George E. (2015)
Statistical Analysis for High-Dimensional Data - The Abel Symposium 2014 Springer Series pdf - EMVS: The EM Approach to Bayesian Variable Selection
Rockova V. and George E. (2014)
Journal of the American Statistical Association, Theory and Methods (109), 828-846 link - Negotiating Multicolinearity with Spike-and-Slab Priors
Rockova V. and George E. (2014)
Metron (72), 217-229 link - Incorporating Grouping in Bayesian Variable Selection with Applications in Genomics
Rockova V. and Lesaffre E. (2014)
Bayesian Analysis (9), 221-258. link - Hierarchical Bayesian Formulations for Selecting Variables in Regression Models
Rockova V., Lesaffre E., Luime, J., Lowenberg, B. (2012)
Statistics in Medicine (31), 1221-1237. link
Public Health and Biomedical
- Improving Medicare's Hospital Compare Mortality Model
Silber, J. H., Satopaa, V. A., Mukherjee, N., Rockova, V. , Wang, W., Hill, A., Even-Shoshan, O., Rosenbaum, P. R., and George, E. (2016)
Health Services Research Journal - Risk-stratification
of Intermediate-risk Acute Myeloid Leukemia: Integrative Analysis of a multitude of
gene mutation and expression markers
Rockova V., Abbas S., Wouters B.J., Erpelinck C., Beverloo B., Delwel R., van Putten W., Lowenberg B. and Valk P. (2011)
Blood (118), 1069-1076 -
The Prognostic Relevance of miR-212 Expression with Survival
in Cytogenetically and Molecularly Heterogeneous AML
Sun S., Rockova V., Bullinger L., Dijkstra M., Dohner H., Lowenberg B., Jongen-Lavrencic M. (2013)
Leukemia (27), 100-106 -
Mutant DNMT3A: a Marker of Poor Prognosis in Acute Myeloid Leukemia
Ribeiro A., Pratcorona M., Erpelinck C., Rockova V., Sanders M., Abbas S., Figueroa M., Zeilemaker Z., Melnick A., Lowenberg B., Valk P. and Delwel R. (2012)
Blood (119), 5824-5831 -
Retroviral Integration Mutagenesis in Mice and Comparative
Analysis in Human AML Identify Reduced PTP4A3 Expression
as a Prognostic Indicator
Beekman E., Valkhof M., Erkeland S., Taskesen E., Rockova V., Peeters J., Valk P., Lowenberg B. and Touw I. (2011)
PLoS ONE 6(10), e26537 -
Deregulated Expression of EVI1 Defines a Poor Prognostic Subset
of MLL-Rearranged Acute Myeloid Leukemias
Groschel S., Schlenk R., Engelmann J., Rockova V., Teleanu V., Kuhn M., Eiwen K., Erpelinck C., Havermans M., Lubbert M., Germing U., Schmidt-Wolf I., Beverloo B., Schuurhuis G., Bargetzi M., Krauter J., Ganser A., Valk P., Lowenberg B., Dohner K., Dohner H., Delwel R. (2013)
Journal of Clinical Oncology 31(1), 95-103
Refereed Proceedings
- Fast Bayesian Factor Analysis with the Indian Buffet Process
Rockova V. and George E. (2014)
47th Scientific Meeting of Italian Statistical Society - Dual Coordinate Ascent EM for Bayesian Variable Selection
George E., Rockova V., Lesaffre E. (2013)
28th International Workshop in Statistical Modeling, ISBN: 978-88-96251-47-8, 165-171 - Sparse Bayesian Factor Regression Approach to Genomic Data Integration
Rockova V. and Lesaffre E. (2013)
28th International Workshop in Statistical Modeling, ISBN: 978-88-96251-47-8, 337-343 - Incorporating Prior Biological Knowledge in Bayesian Modeling of Sparse Networks
Rockova V. and Lesaffre E. (2012)
27th International Workshop in Statistical Modeling, ISBN: 978-80-263-0250-6, 291-296
"Machinarium"
BB-SSL
This R package implements BB-SSL (Bayesian Bootstrap
Spike-and-Slab LASSO) from Nie and
Rockova (2021). BB-SSL is an approximate posterior
sampling strategy for the Spike-and-Slab LASSO.
See documentation and examples.
TVS
This R package implements TVS (Thompson sampling
for variable selection) from Liu and
Rockova (2021). TVS is a reinforcement learning
algorithm for Bayesian subset selection.
See examples.
EMVS
C++ written R package implementing an EM
algorithm for Bayesian variable selection described
in Rockova and George (2014). The software is made available as is, and no warranty -
about the software, its performance or its conformity to any
specification - is given or implied. Please email me with
comments
and suggestions.
The package can be installed via R CMD BUILD and R CMD INSTALL from a local R library directory.
Now available on CRAN!
Check out help(EMVS) for examples.
Spike-and-Slab LASSO
C written R package implementing coordinate-wise optimization for Spike-and-Slab LASSO priors in linear regression (Rockova and George (2015)). Spike-and-Slab LASSO is a spike-and-slab refinement of the LASSO procedure, using a mixture of Laplace priors indexed by lambda0 (spike) and lambda1 (slab). The SSLASSO procedure fits coefficients paths for Spike-and-Slab LASSO-penalized linear regression models over a grid of values for the regularization parameter lambda_0.
Now available on CRAN!
Check out help(SSLASSO) for examples.
Factor Rotations to Sparsity
R code for implementing rotations to sparsity in
high-dimensional factor models (Rockova
and George (2015)). FACTOR ROTATE is a unified Bayesian approach that incorporates factor rotations within the model fitting process, greatly enhancing the effectiveness of sparsity inducing priors. These automatic transformations are embedded within a new PXL-EM algorithm, a Bayesian variant of parameter-expanded EM for fast posterior mode detection.
Particle EM
C written R package implementing Particle EM of Rockova (2017), a new population-based optimization strategy that harvests multiple modes in search spaces that present many local maxima. Motivated by non-parametric variational Bayes strategies, Particle EM achieves this goal by deploying an ensemble of interactive repulsive particles. These particles are geared towards uncharted areas of the posterior, providing a more comprehensive summary of its topography than simple parallel EM deployments.My Team
Jungeum Kim, Ph.D.
Jungeum Kim is a Principal Researcher in Econometrics and
Statistics at the University of Chicago Booth School of Business. She earned her Ph.D. from Purdue University under Dr. Xiao Wang's guidance. She obtained her M.A. and B.A. from Seoul National University, supervised by Dr. Hee-Seok Oh. Her research focuses on integrating deep learning into Bayesian statistics with theoretical guarantees, ensuring deep learning is safe and accessible across various fields. Her interests also include nonparametric Bayes, clustering, and dimension reduction. Personal Website
Jiguang Li
Jiguang is a 2nd-year PhD student in Econometrics and Statistics at
Booth, with a broad research interest in Bayesian statistics, machine learning, and factor analysis (item response theory). Previously, he worked as a full-time research professional at the Center for Applied AI under the supervision of Sendhil Mullainathan. Jiguang holds a master's degree in statistics from Yale and a bachelor's degree in mathematics from Middlebury College.
Sean O'Hagan
Sean is a third year PhD student in statistics at the University of Chicago, supported by a McCormick fellowship. He is interested in Bayesian methodology, online learning, and applications to the physical and social sciences. His research currently focuses on using modern tools to accelerate likelihood-free Bayesian inference. Sean obtained a B.S. in mathematics and in statistics at the University of Connecticut.
Percy Zhai
Percy is a 3rd-year PhD student in Econometrics and Statistics at Booth. His research interest encompasses Bayesian statistics, time series, and high-dimensional inference. Percy obtained his M.S. degree in statistics from the University of Chicago, and B.S. degree in physics from University of Science and Technology of China.
Alumni
Yuexi Wang, PhD
is an Assistant Professor at the Department of Statistics at
the University of Illinois at Urbana-Champaign.
Seonghyun Jeong, Ph.D.
is
an
Assistant
Professor
at
the
Department of Statistics and Data Science and the
Department of Applied Statistics at
Yonsei University, Seoul, Korea.
Enakshi Saha, PhD
is a Research Associate at Harvard University T. H. Chan School of Public Health, Department of Biostatistics.
Lizhen
Nie, PhD
is a quantitative researcher at Two Sigma.
Yi Liu, PhD
is an applied scientist at Amazon.