Preprints
N. Ramezani and M. Slawski, "Lasso Penalization for High-Dimensional Beta Regression Models:
Computation, Analysis, and Inference" [arxiv link]
F. Balabdaoui, M. Slawski, and J. Steffani , "Identifiability in Unlinked Linear Regression: Some Results and Open Problems" [arxiv link]
J. Auerbach, M. Slawski, and S. Zhang, "Reevaluating COVID-19 Mandates using Tensor Completion" [arxiv link]
Journal Papers
E. Fabrizi, N. Salvati, M. Slawski, Accouting for Mismatch Error in Small Area Estimation with Linked Data, Journal of Survey Statistics and Methodology, available online [journal link][arxiv link]
B.T. West, M. Slawski, and E. Ben-David. Improved Ensemble Predictive Modeling Techniques for Linked Social Media and Survey Data Sets Subject to Mismatch Error, Methods, Data, Analyses (MDA), available online. [journal link]
M. Slawski, B.T. West, P. Bukke, Z. Wang, G. Diao, and E. Ben-David. A General Framework for Regression with Mismatched Data Based on Mixture Modeling, Journal of the Royal Statistical Society Series A, 188(3), 896-919 [journal link][arxiv link][R package]
M. Slawski and B. Sen, "Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport", Journal of Machine Learning Research, 25(183):1−57. [journal link]
X. Zhu, M. Slawski, L. Tang "A Framework for Covariate-Specific ROC Curve Estimation, with Application to Biometric Recognition", Annals of Applied Statistics, 17(4): 2821-2842 [journal link]
Z. Wang, E. Ben-David, M. Slawski, "Estimation in exponential family regression based on linked data contaminated by mismatch error", Statistics and Its Interface, 16, 379-396, [arxiv link] [journal link]
Z. Wang, E. Ben-David, G. Diao, M. Slawski, "Regression with linked data sets subject to linkage error" WIREs Computational Statistics, e1570,[journal link]
H. Zhang, M. Slawski, P. Li, "The Benefits of Diversity: Permutation Recovery in Unlabeled Sensing from Multiple Measurement Vectors", IEEE Transactions on Information Theory, 68(4), 2509 - 2529. [arxiv link] [journal link]
M. Slawski, G. Diao, E. Ben-David, "A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data", Journal of Computational & Graphical Statistics, 30, 991-1003, 2021. [arxiv link][journal link]
X. Zhu, M. Slawski#, L. Tang, J. Phillips, "Order-Constrained ROC Regression with Application to Facial Recognition", Technometrics, 63(3), 343-353, 2021. [journal link] #corresponding author
Q. Li, A. Alipour-Fanid, M. Slawski, Y. Ye, K. Zeng, L. Wu, L. Zhao, "Large-scale Cost-aware Classification Using Feature Computational Dependency Graph", IEEE Transactions on Knowledge and Data Engineering, 33(5), 2029-2044, 2021. [journal link]
M. Slawski, E. Ben-David, P. Li, "A Two-Stage Approach to Multivariate Linear Regression with Sparsely Permuted Data" [link][talk slides], Journal of Machine Learning Research, 21(204):1-42, 2020.
F. Detmer, J. Cebral, M. Slawski#, "A Note on Coding and Standardization of Categorical Variables in (Sparse) Group Lasso Regression" [arxiv link][journal link][CRAN], Journal of Statistical Planning & Inference, 206, 1-11, 2020. #corresponding author
P. Li*, S. Rangapuram*, M. Slawski*, "Methods for sparse and low-rank recovery under simplex constraints", Statistica Sinica, 30, 557-577, 2020. [arxiv link][final version][supplement] *authors are in lexicographical order
M. Slawski, E. Ben-David, "Linear Regression with Sparsely Permuted Data", Electronic Journal of Statistics, 13, 1-36, 2019. [.pdf]
M. Slawski, "On Principal Components Regression, Random Projections, and Column Subsampling", Electronic Journal of Statistics, 12, 3673-3712, 2018. [.pdf]
M. Slawski and P. Li, "On the Trade-off Between Bit Depth and Number of Samples for a Basic Approach to Structured Signal Recovery from b-bit Quantized Linear Measurements", IEEE Transactions on Information Theory, 64, 4159-4178, 2018 [link]
P. Lutsik, M. Slawski, G. Gasparoni, N. Vedeneev, M. Hein, J. Walter, "MeDeCom: discovery and quantification of latent components of heterogeneous methylomes", Genome Biology, 2017, 18:55. [link]
M. Slawski and M. Hein, "Positive definite M-matrices and structure learning in attractive Gaussian Markov random fields", Linear Algebra and its Applications, 473, 145-179, 2015. [pdf] [code]
M. Slawski and M. Hein, "Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization",Electronic Journal of Statistics, 7, 3004-3056, 2013 [pdf][supplement]
M. Slawski, R. Hussong, A. Tholey, T. Jakoby, B. Gregorius, A. Hildebrandt, M. Hein, "Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching", BMC Bioinformatics, 2012, 13:291 (8 November 2012) [link][code]
M. Slawski, "The structured elastic net for quantile regression and support vector classification", Statistics and Computing, 22, 153-168, 2012. [pdf][code]
M. Slawski, W. zu Castell, G. Tutz, "Feature Selection Guided by Structural Information" , Annals of Applied Statistics, 4(2), 1056-1080, 2010. [pdf][supplement][code]
A.-L. Boulesteix and M. Slawski, "Stability and Aggregation of ranked gene lists", Briefings in Bioinformatics, 10(5), 556-568, 2009. [link][code]
M. Slawski, M. Daumer, A.-L. Boulesteix, "CMA - a comprehensive Bioconductor package for supervised classification with high-dimensional data", BMC Bioinformatics, 2008, 9:439 (16 October 2008) [link] [code]
Proceedings
Z. Wang, E. Ben-David, M. Slawski, "Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group", Artificial Intelligence & Statistics (AISTATS) 2023 [link]
Y. Chen, Y. Ning, M. Slawski, H. Rangwala, "Asynchronous Online Federated Learning for Edge Devices" [arxiv link], IEEE Big Data 2021.
M. Slawski, M. Rahmani, and P. Li, "A Robust Subspace Recovery Approach to Linear Regression with Partially Shuffled Labels", in Uncertainty in Artificial Intelligence (UAI 2019). [.pdf][video]
L. Zhao, A. Alipour-Fanid, M. Slawski, K. Zeng, "Prediction-time Efficient Classification Using Computational Dependencies in Feature Generation", Knowledge Discovery and Data Mining (KDD 2018) [link]
P. Li*, M. Slawski*, "Simple strategies for recovering inner products from coarsely quantized random projections", Advances in Neural Information Processing Systems 30 (NIPS 2017) [link]
M. Slawski, "Compressed Least Squares Regression revisited", Artificial Intelligence and Statistics (AISTATS 2017) [link]
P. Li*, M. Mitzenmacher*, M. Slawski*, "Quantized Random Projections and Non-Linear Estimation of Cosine Similarity", Advances in Neural Information Processing Systems 29 (NIPS 2016) [link]
M. Slawski and P. Li, "b-bit Marginal Regression", Advances in Neural Information Processing Systems 28 (NIPS 2015) [link]
M. Slawski, P. Li and M. Hein, "Regularization-free estimation in trace regression with positive definite matrices",
Advances in Neural Information Processing Systems 28 (NIPS 2015) [link] [arxiv link]
M. Slawski, M. Hein, P. Lutsik, "Matrix Factorization with Binary Components", Advances in Neural Information Processing Systems 26 (NIPS 2013) [pdf][supplement]
M. Slawski and M. Hein, "Sparse recovery by thresholded non-negative least squares", Advances in Neural Information Processing Systems 24 (NIPS 2011) [pdf][supplement]
M. Slawski and M. Hein, "Robust sparse recovery with non-negativity constraints", 4th Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS), 2011 [talk slides]
Book chapter
P. Bukke, E. Ben-David, G. Diao, M. Slawski, and B.T. West. Cox Proportional Hazards Regression Using Linked Data: An Approach Based on Mixture Modelling, in "Frontiers of Statistics and Data Science". IISA Series on Statistics and Data Science, Springer.
M. Slawski and M. Hein, "Sparse Recovery for Protein Mass Spectrometry Data" in "Practical Applications of Sparse Modeling", edited by I. Rish, G. Cecchi, A. Lozano, A. Niculescu-Mizil, MIT press.
Working papers
M. Slawski, "Random polyhedral cones with many faces and compressed sensing of non-negative signals: a restricted eigenvalue approach" (October 2013; revised April 2015) [pdf]
M. Slawski, "Problem-specific analysis of non-negative least squares solvers with a focus on instances with sparse solutions" (March 2013) [pdf][code]
M. Slawski*, Q. Zheng*, M. Hein "Sparse Matrix Factorization over the probability simplex: geometry, sparsification, and adaptation to the latent dimension" (October 2012; revised December 2013) [pdf] *contributed equally
Theses
M. Slawski, "Topics in learning sparse and low-rank models of non-negative data", PhD Dissertation, Saarland University (April 2015) [pdf]
M. Slawski, "Regularization and Sparsity in discrete structures", Diploma Thesis, LMU Munich (November 2008) [pdf]