| A framework for Bayesian optimization in embedded subspaces A Nayebi, A Munteanu, M Poloczek International Conference on Machine Learning, 4752-4761, 2019 | 143 | 2019 |
| On coresets for logistic regression A Munteanu, C Schwiegelshohn, C Sohler, D Woodruff Advances in Neural Information Processing Systems 31, 2018 | 112 | 2018 |
| Coresets-methods and history: A theoreticians design pattern for approximation and streaming algorithms A Munteanu, C Schwiegelshohn KI-Künstliche Intelligenz 32, 37-53, 2018 | 86 | 2018 |
| Random projections for Bayesian regression LN Geppert, K Ickstadt, A Munteanu, J Quedenfeld, C Sohler Statistics and Computing 27, 79-101, 2017 | 53 | 2017 |
| Smallest enclosing ball for probabilistic data A Munteanu, C Sohler, D Feldman Proceedings of the thirtieth annual symposium on Computational geometry, 214-223, 2014 | 45 | 2014 |
| Core dependency networks A Molina, A Munteanu, K Kersting Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 31 | 2018 |
| Oblivious sketching for logistic regression A Munteanu, S Omlor, D Woodruff International Conference on Machine Learning, 7861-7871, 2021 | 25 | 2021 |
| Bounding the Width of Neural Networks via Coupled Initialization - A Worst Case Analysis A Munteanu, S Omlor, Z Song, DP Woodruff arXiv preprint arXiv:2206.12802, 2022 | 19 | 2022 |
| Random projections and sampling algorithms for clustering of high-dimensional polygonal curves S Meintrup, A Munteanu, D Rohde Advances in Neural Information Processing Systems 32, 2019 | 14 | 2019 |
| Streaming statistical models via Merge & Reduce LN Geppert, K Ickstadt, A Munteanu, C Sohler International Journal of Data Science and Analytics 10, 331-347, 2020 | 10 | 2020 |
| Almost Linear Constant-Factor Sketching for and Logistic Regression A Munteanu, S Omlor, D Woodruff arXiv preprint arXiv:2304.00051, 2023 | 9 | 2023 |
| -Generalized probit regression and scalable maximum likelihood estimation via sketching and coresets A Munteanu, S Omlor, C Peters International Conference on Artificial Intelligence and Statistics, 2073-2100, 2022 | 8 | 2022 |
| 3.2 Coresets and Sketches for Regression Problems on Data Streams and Distributed Data A Munteanu Machine Learning under Resource Constraints - Fundamentals 1, 85–98, 2023 | 7 | 2023 |
| Probabilistic smallest enclosing ball in high dimensions via subgradient sampling A Krivošija, A Munteanu arXiv preprint arXiv:1902.10966, 2019 | 6 | 2019 |
| Asymptotically exact streaming algorithms M Heinrich, A Munteanu, C Sohler arXiv preprint arXiv:1408.1847, 2014 | 6 | 2014 |
| Correcting statistical models via empirical distribution functions A Munteanu, M Wornowizki Computational Statistics 31, 465-495, 2016 | 5* | 2016 |
| Optimal sketching bounds for sparse linear regression T Mai, A Munteanu, C Musco, A Rao, C Schwiegelshohn, D Woodruff International Conference on Artificial Intelligence and Statistics, 11288-11316, 2023 | 4 | 2023 |
| On large-scale probabilistic and statistical data analysis A Munteanu PhD Thesis, TU Dortmund University, 2018 | 3 | 2018 |
| Scalable learning of item response theory models S Frick, A Krivosija, A Munteanu International Conference on Artificial Intelligence and Statistics, 1234-1242, 2024 | 2 | 2024 |
| Cross-leverage scores for selecting subsets of explanatory variables K Parry, LN Geppert, A Munteanu, K Ickstadt arXiv preprint arXiv:2109.08399, 2021 | 2 | 2021 |
Christian SohlerProfessor for Algorithmic Data Analysis, University of Cologne在 uni-koeln.de 的电子邮件经过验证
