CMA - a comprehensive Bioconductor package for supervised classification with high dimensional data. - Martin Slawski; Martin Daumer; Anne-Laure Boulesteix (BMC Bioinform., 2008)

Stability and aggregation of ranked gene lists. - Anne-Laure Boulesteix; Martin Slawski (Briefings Bioinform., 2009)

Sparse recovery by thresholded non-negative least squares. - Martin Slawski; Matthias Hein 001 (NIPS, 2011)

Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching. - Martin Slawski; Rene Hussong; Andreas Tholey; Thomas Jakoby; Barbara Gregorius; Andreas Hildebrandt 001; Matthias Hein 001 (BMC Bioinform., 2012)

The structured elastic net for quantile regression and support vector classification. - Martin Slawski (Stat. Comput., 2012)

Matrix factorization with binary components. - Martin Slawski; Matthias Hein 001; Pavlo Lutsik (NIPS, 2013)

b-bit Marginal Regression. - Martin Slawski; Ping Li 001 (NIPS, 2015)

Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices. - Martin Slawski; Ping Li 001; Matthias Hein 001 (NIPS, 2015)

Topics in learning sparse and low-rank models of non-negative data. - Martin Slawski (2015)

Quantized Random Projections and Non-Linear Estimation of Cosine Similarity. - Ping Li 001; Michael Mitzenmacher; Martin Slawski (NIPS, 2016)

Methods for Sparse and Low-Rank Recovery under Simplex Constraints. - Ping Li 001; Syama Sundar Rangapuram; Martin Slawski (CoRR, 2016)

Linear signal recovery from b-bit-quantized linear measurements: precise analysis of the trade-off between bit depth and number of measurements. - Martin Slawski; Ping Li 001 (CoRR, 2016)

Compressed Least Squares Regression revisited. - Martin Slawski (AISTATS, 2017)

Simple strategies for recovering inner products from coarsely quantized random projections. - Ping Li 001; Martin Slawski (NIPS, 2017)

Prediction-time Efficient Classification Using Feature Computational Dependencies. - Liang Zhao 002; Amir Alipour-Fanid; Martin Slawski; Kai Zeng 001 (KDD, 2018)

Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics. - Felicitas J. Detmer; Bong Jae Chung; Fernando Mut; Martin Slawski; Farid Hamzei-Sichani; Christopher M. Putman; Carlos Jiménez; Juan R. Cebral (Int. J. Comput. Assist. Radiol. Surg., 2018)

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. - Martin Slawski; Ping Li 001 (IEEE Trans. Inf. Theory, 2018)

Permutation Recovery from Multiple Measurement Vectors in Unlabeled Sensing. - Hang Zhang; Martin Slawski; Ping Li 001 (ISIT, 2019)

A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels. - Martin Slawski; Mostafa Rahmani; Ping Li 001 (UAI, 2019)

Asynchronous Online Federated Learning for Edge Devices with Non-IID Data. - Yujing Chen; Yue Ning 001; Martin Slawski; Huzefa Rangwala (IEEE BigData, 2020)

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment. - Felicitas J. Detmer; Daniel Lückehe; Fernando Mut; Martin Slawski; Sven Hirsch; Philippe Bijlenga; Gabriele von Voigt; Juan R. Cebral (Int. J. Comput. Assist. Radiol. Surg., 2020)

Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data. - Martin Slawski; Emanuel Ben-David; Ping Li 001 (J. Mach. Learn. Res., 2020)

A Pseudo-Likelihood Approach to Linear Regression With Partially Shuffled Data. - Martin Slawski; Guoqing Diao; Emanuel Ben-David (J. Comput. Graph. Stat., 2021)

Order-Constrained ROC Regression With Application to Facial Recognition. - Xiaochen Zhu 002; Martin Slawski; P. Jonathon Phillips; Liansheng Larry Tang (Technometrics, 2021)

Large-scale Cost-Aware Classification Using Feature Computational Dependency Graph. - Qingzhe Li; Amir Alipour-Fanid; Martin Slawski; Yanfang Ye 001; Lingfei Wu 001; Kai Zeng 001; Liang Zhao 002 (IEEE Trans. Knowl. Data Eng., 2021)

Permuted and Unlinked Monotone Regression in Rd: an approach based on mixture modeling and optimal transport. - Martin Slawski; Bodhisattva Sen (CoRR, 2022)

The Benefits of Diversity: Permutation Recovery in Unlabeled Sensing From Multiple Measurement Vectors. - Hang Zhang; Martin Slawski; Ping Li 001 (IEEE Trans. Inf. Theory, 2022)

Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group. - Zhenbang Wang; Emanuel Ben-David; Martin Slawski (AISTATS, 2023)

Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport. - Martin Slawski; Bodhisattva Sen (J. Mach. Learn. Res., 2024)