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ORCIDAlexander Munteanu
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2020 – today
- 2024
[c16]Susanne Frick, Amer Krivosija, Alexander Munteanu:
Scalable Learning of Item Response Theory Models. AISTATS 2024: 1234-1242- [c15]Henrike Weinert, Alexander Munteanu:
TU Dortmund - Center for Data Science & Simulation: Data Literacy Education an der TU Dortmund. INFORMATIK 2024: 2205-2211 - [c14]Alexander Munteanu, Simon Omlor:
Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation. ICML 2024 - [c13]Alexander Munteanu, Simon Omlor:
Turnstile ℓp leverage score sampling with applications. ICML 2024 - [i15]Susanne Frick, Amer Krivosija, Alexander Munteanu:
Scalable Learning of Item Response Theory Models. CoRR abs/2403.00680 (2024) - [i14]Alexander Munteanu, Simon Omlor:
Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation. CoRR abs/2406.00328 (2024) - [i13]Alexander Munteanu, Simon Omlor:
Turnstile ℓp leverage score sampling with applications. CoRR abs/2406.00339 (2024) - [i12]Han Cheng Lie, Alexander Munteanu:
Data subsampling for Poisson regression with pth-root-link. CoRR abs/2410.22872 (2024) - 2023
- [c12]Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. AISTATS 2023: 11288-11316 - [c11]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Almost Linear Constant-Factor Sketching for $\ell_1$ and Logistic Regression. ICLR 2023 - [i11]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Almost Linear Constant-Factor Sketching for 𝓁1 and Logistic Regression. CoRR abs/2304.00051 (2023) - [i10]Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. CoRR abs/2304.02261 (2023) - 2022
- [c10]Alexander Munteanu, Simon Omlor, Christian Peters:
p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets. AISTATS 2022: 2073-2100 - [c9]Alexander Munteanu, Simon Omlor, Zhao Song, David P. Woodruff:
Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis. ICML 2022: 16083-16122 - [p2]Zeyu Ding, Katja Ickstadt, Alexander Munteanu:
Bayesian Analysis for Dimensionality and Complexity Reduction. Mach. Learn. under Resour. Constraints Vol. 3 (3) 2022: 58-70 - [p1]Alexander Munteanu:
Coresets and Sketches for Regression Problems on DataStreams and Distributed Data. Mach. Learn. under Resour. Constraints Vol. 1 (1) 2022: 85-97 - [i9]Alexander Munteanu, Simon Omlor, Christian Peters:
p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets. CoRR abs/2203.13568 (2022) - [i8]Alexander Munteanu, Simon Omlor, Zhao Song, David P. Woodruff:
Bounding the Width of Neural Networks via Coupled Initialization - A Worst Case Analysis. CoRR abs/2206.12802 (2022) - 2021
- [c8]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Oblivious Sketching for Logistic Regression. ICML 2021: 7861-7871 - [i7]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Oblivious sketching for logistic regression. CoRR abs/2107.06615 (2021) - 2020
- [j4]Leo N. Geppert, Katja Ickstadt, Alexander Munteanu
, Christian Sohler:
Streaming statistical models via Merge & Reduce. Int. J. Data Sci. Anal. 10(4): 331-347 (2020)
2010 – 2019
- 2019
- [c7]Amer Krivosija, Alexander Munteanu:
Probabilistic Smallest Enclosing Ball in High Dimensions via Subgradient Sampling. SoCG 2019: 47:1-47:14 - [c6]Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. GI-Jahrestagung 2019: 267-268 - [c5]Amin Nayebi, Alexander Munteanu, Matthias Poloczek:
A Framework for Bayesian Optimization in Embedded Subspaces. ICML 2019: 4752-4761 - [c4]Stefan Meintrup, Alexander Munteanu, Dennis Rohde:
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves. NeurIPS 2019: 12807-12817 - [i6]Amer Krivosija, Alexander Munteanu:
Probabilistic smallest enclosing ball in high dimensions via subgradient sampling. CoRR abs/1902.10966 (2019) - [i5]Stefan Meintrup, Alexander Munteanu, Dennis Rohde:
Random projections and sampling algorithms for clustering of high-dimensional polygonal curves. CoRR abs/1907.06969 (2019) - 2018
- [b1]Alexander Munteanu:
On large-scale probabilistic and statistical data analysis. Dortmund University, Germany, 2018 - [j3]Alexander Munteanu, Chris Schwiegelshohn:
Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms. Künstliche Intell. 32(1): 37-53 (2018) - [c3]Alejandro Molina, Alexander Munteanu, Kristian Kersting:
Core Dependency Networks. AAAI 2018: 3820-3827 - [c2]Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. NeurIPS 2018: 6562-6571 - [i4]Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. CoRR abs/1805.08571 (2018) - 2017
- [j2]Leo N. Geppert, Katja Ickstadt, Alexander Munteanu, Jens Quedenfeld, Christian Sohler:
Random projections for Bayesian regression. Stat. Comput. 27(1): 79-101 (2017) - [i3]Alejandro Molina, Alexander Munteanu, Kristian Kersting:
Coresets for Dependency Networks. CoRR abs/1710.03285 (2017) - 2016
- [j1]Alexander Munteanu, Max Wornowizki:
Correcting statistical models via empirical distribution functions. Comput. Stat. 31(2): 465-495 (2016) - 2015
- [i2]Leo N. Geppert, Katja Ickstadt, Alexander Munteanu, Jens Quedenfeld, Christian Sohler:
Random projections for Bayesian regression. CoRR abs/1504.06122 (2015) - 2014
- [c1]Alexander Munteanu, Christian Sohler, Dan Feldman:
Smallest enclosing ball for probabilistic data. SoCG 2014: 214 - [i1]Marc Heinrich, Alexander Munteanu, Christian Sohler:
Asymptotically exact streaming algorithms. CoRR abs/1408.1847 (2014)
Coauthor Index
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