Smallest enclosing ball for probabilistic data. - Alexander Munteanu; Christian Sohler; Dan Feldman (SoCG, 2014)

Asymptotically exact streaming algorithms. - Marc Heinrich; Alexander Munteanu; Christian Sohler (CoRR, 2014)

Correcting statistical models via empirical distribution functions. - Alexander Munteanu; Max Wornowizki (Comput. Stat., 2016)

Coresets for Dependency Networks. - Alejandro Molina 001; Alexander Munteanu; Kristian Kersting (CoRR, 2017)

Random projections for Bayesian regression. - Leo N. Geppert; Katja Ickstadt; Alexander Munteanu; Jens Quedenfeld; Christian Sohler (Stat. Comput., 2017)

Core Dependency Networks. - Alejandro Molina 001; Alexander Munteanu; Kristian Kersting (AAAI, 2018)

On Coresets for Logistic Regression. - Alexander Munteanu; Chris Schwiegelshohn; Christian Sohler; David P. Woodruff (NeurIPS, 2018)

Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms. - Alexander Munteanu; Chris Schwiegelshohn (Künstliche Intell., 2018)

On large-scale probabilistic and statistical data analysis - Alexander Munteanu (2018)

Probabilistic Smallest Enclosing Ball in High Dimensions via Subgradient Sampling. - Amer Krivosija; Alexander Munteanu (SoCG, 2019)

A Framework for Bayesian Optimization in Embedded Subspaces. - Amin Nayebi; Alexander Munteanu; Matthias Poloczek (ICML, 2019)

Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves. - Stefan Meintrup; Alexander Munteanu; Dennis Rohde (NeurIPS, 2019)

Streaming statistical models via Merge & Reduce. - Leo N. Geppert; Katja Ickstadt; Alexander Munteanu; Christian Sohler (Int. J. Data Sci. Anal., 2020)

Oblivious Sketching for Logistic Regression. - Alexander Munteanu; Simon Omlor; David P. Woodruff (ICML, 2021)

Bayesian Analysis for Dimensionality and Complexity Reduction. - Zeyu Ding 007; Katja Ickstadt; Alexander Munteanu (Mach. Learn. under Resour. Constraints Vol. 3 (3), 2022)

Coresets and Sketches for Regression Problems on DataStreams and Distributed Data. - Alexander Munteanu (Mach. Learn. under Resour. Constraints Vol. 1 (1), 2022)

p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets. - Alexander Munteanu; Simon Omlor; Christian Peters (AISTATS, 2022)

Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis. - Alexander Munteanu; Simon Omlor; Zhao Song 002; David P. Woodruff (ICML, 2022)

Optimal Sketching Bounds for Sparse Linear Regression. - Tung Mai; Alexander Munteanu; Cameron Musco; Anup Rao 002; Chris Schwiegelshohn; David P. Woodruff (AISTATS, 2023)

Almost Linear Constant-Factor Sketching for $ ell_1$ and Logistic Regression. - Alexander Munteanu; Simon Omlor; David P. Woodruff (ICLR, 2023)

Almost Linear Constant-Factor Sketching for 𝓁1 and Logistic Regression. - Alexander Munteanu; Simon Omlor; David P. Woodruff (CoRR, 2023)

Scalable Learning of Item Response Theory Models. - Susanne Frick; Amer Krivosija; Alexander Munteanu (AISTATS, 2024)

TU Dortmund - Center for Data Science & Simulation: Data Literacy Education an der TU Dortmund. - Henrike Weinert; Alexander Munteanu (INFORMATIK, 2024)

Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation. - Alexander Munteanu; Simon Omlor (ICML, 2024)

Turnstile ℓp leverage score sampling with applications. - Alexander Munteanu; Simon Omlor (ICML, 2024)

Data subsampling for Poisson regression with pth-root-link. - Han Cheng Lie; Alexander Munteanu (NeurIPS, 2024)

Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation. - Alexander Munteanu; Simon Omlor (CoRR, 2024)

Turnstile ℓp leverage score sampling with applications. - Alexander Munteanu; Simon Omlor (CoRR, 2024)

Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves. - Amer Krivosija; Alexander Munteanu; André Nusser; Chris Schwiegelshohn (CoRR, 2025)