| 2026 | ESA | Dimension Reduction for Curves: Simplified and Generalized. | Matthijs Ebbens, Jie Lu, Alexander Munteanu |
| 2025 | ICML | Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves. | Amer Krivosija, Alexander Munteanu, Andr Nusser, Chris Schwiegelshohn |
| 2024 | AISTATS | Scalable Learning of Item Response Theory Models. | Susanne Frick, Amer Krivosija, Alexander Munteanu |
| 2024 | GI | TU Dortmund - Center for Data Science & Simulation: Data Literacy Education an der TU Dortmund. | Henrike Weinert, Alexander Munteanu |
| 2024 | ICML | Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation. | Alexander Munteanu, Simon Omlor |
| 2024 | ICML | Turnstile ℓp leverage score sampling with applications. | Alexander Munteanu, Simon Omlor |
| 2023 | AISTATS | Optimal Sketching Bounds for Sparse Linear Regression. | Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff |
| 2023 | ICLR | Almost Linear Constant-Factor Sketching for $\ell_1$ and Logistic Regression. | Alexander Munteanu, Simon Omlor, David P. Woodruff |
| 2022 | AISTATS | p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets. | Alexander Munteanu, Simon Omlor, Christian Peters |
| 2022 | ICML | Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis. | Alexander Munteanu, Simon Omlor, Zhao Song, David P. Woodruff |
| 2021 | ICML | Oblivious Sketching for Logistic Regression. | Alexander Munteanu, Simon Omlor, David P. Woodruff |
| 2019 | GI | On Coresets for Logistic Regression. | Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff |
| 2019 | ICML | A Framework for Bayesian Optimization in Embedded Subspaces. | Amin Nayebi, Alexander Munteanu, Matthias Poloczek |
| 2018 | AAAI | Core Dependency Networks. | Alejandro Molina, Alexander Munteanu, Kristian Kersting |