Munteanu, Alexander

14 publications

ICML 2025 Improved Learning via K-DTW: A Novel Dissimilarity Measure for Curves Amer Krivošija, Alexander Munteanu, André Nusser, Chris Schwiegelshohn

NeurIPS 2024 Data Subsampling for Poisson Regression with Pth-Root-Link Han Cheng Lie, Alexander Munteanu

ICML 2024 Optimal Bounds for $\ell_p$ Sensitivity Sampling via $\ell_2$ Augmentation Alexander Munteanu, Simon Omlor

AISTATS 2024 Scalable Learning of Item Response Theory Models Susanne Frick, Amer Krivosija, Alexander Munteanu

ICML 2024 Turnstile $\ell_p$ Leverage Score Sampling with Applications Alexander Munteanu, Simon Omlor

ICLR 2023 Almost Linear Constant-Factor Sketching for $\ell_1$ and Logistic Regression Alexander Munteanu, Simon Omlor, David Woodruff

AISTATS 2023 Optimal Sketching Bounds for Sparse Linear Regression Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David Woodruff

AISTATS 2022 P-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets Alexander Munteanu, Simon Omlor, Christian Peters

ICML 2022 Bounding the Width of Neural Networks via Coupled Initialization a Worst Case Analysis Alexander Munteanu, Simon Omlor, Zhao Song, David Woodruff

ICML 2021 Oblivious Sketching for Logistic Regression Alexander Munteanu, Simon Omlor, David Woodruff

ICML 2019 A Framework for Bayesian Optimization in Embedded Subspaces Amin Nayebi, Alexander Munteanu, Matthias Poloczek

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

AAAI 2018 Core Dependency Networks Alejandro Molina, Alexander Munteanu, Kristian Kersting

NeurIPS 2018 On Coresets for Logistic Regression Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David Woodruff