Determinantal Regularization for Ensemble Variable Selection. - Veronika Rocková; Gemma E. Moran; Edward I. George (AISTATS, 2016)

Bayesian Dyadic Trees and Histograms for Regression. - Stéphanie van der Pas; Veronika Rocková (NIPS, 2017)

Posterior Concentration for Sparse Deep Learning. - Veronika Rocková; Nicholas Polson (NeurIPS, 2018)

On Theory for BART. - Veronika Rocková; Enakshi Saha (AISTATS, 2019)

Uncertainty Quantification for Sparse Deep Learning. - Yuexi Wang; Veronika Rocková (AISTATS, 2020)

On Semi-parametric Inference for BART. - Veronika Rocková (ICML, 2020)

Variable Selection via Thompson Sampling. - Yi Liu 072; Veronika Rocková (CoRR, 2020)

Comment: Regularization via Bayesian Penalty Mixing. - Edward I. George; Veronika Rocková (Technometrics, 2020)

Adaptive Bayesian SLOPE: Model Selection With Incomplete Data. - Wei Jiang 027; Malgorzata Bogdan; Julie Josse; Szymon Majewski; Blazej Miasojedow; Veronika Rocková (J. Comput. Graph. Stat., 2022)

Approximate Bayesian Computation via Classification. - Yuexi Wang; Tetsuya Kaji; Veronika Rocková (J. Mach. Learn. Res., 2022)

The Art of BART: Minimax Optimality over Nonhomogeneous Smoothness in High Dimension. - Seonghyun Jeong; Veronika Rocková (J. Mach. Learn. Res., 2023)

Tree Bandits for Generative Bayes. - Sean O'Hagan; Jungeum Kim; Veronika Rocková (CoRR, 2024)

Adaptive Uncertainty Quantification for Generative AI. - Jungeum Kim; Sean O'Hagan; Veronika Rocková (CoRR, 2024)

Deep Generative Quantile Bayes. - Jungeum Kim; Percy S. Zhai; Veronika Rocková (AISTATS, 2025)

AI-Powered Bayesian Inference. - Veronika Rocková; Sean O'Hagan (CoRR, 2025)

From Small to Large Language Models: Revisiting the Federalist Papers. - So Won Jeong; Veronika Rocková (CoRR, 2025)