Martin Slawski

发表

b-bit Marginal Regression

Ping Li, Martin Slawski, Ping Li, 2015, NIPS.

Order-Constrained ROC Regression With Application to Facial Recognition

P. Jonathon Phillips, Martin Slawski, Xiaochen Zhu, 2020, Technometrics.

Simple strategies for recovering inner products from coarsely quantized random projections

Ping Li, Martin Slawski, 2017, NIPS.

A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels

Ping Li, Mostafa Rahmani, Martin Slawski, 2019, UAI.

Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices

Matthias Hein, Ping Li, Martin Slawski, 2015, NIPS.

Topics in learning sparse and low-rank models of non-negative data

Martin Slawski, M. Slawski, 2014 .

A Pseudo-Likelihood Approach to Linear Regression With Partially Shuffled Data pdf

Guoqing Diao, Martin Slawski, Emanuel Ben-David, 2019, J. Comput. Graph. Stat..

A Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data pdf

Ping Li, Martin Slawski, Emanuel Ben-David, 2019, ArXiv.

Sparse Dictionary Learning with Simplex Constraints and Application to Topic Modeling

Matthias Hein, Qinqing Zheng, Martin Slawski, 2012 .

Compressed Least Squares Regression revisited

Martin Slawski, 2017, AISTATS.

Permutation Recovery from Multiple Measurement Vectors in Unlabeled Sensing

Ping Li, Hang Zhang, Martin Slawski, 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

Methods for Sparse and Low-Rank Recovery under Simplex Constraints pdf

Ping Li, Syama Sundar Rangapuram, Martin Slawski, 2016, Statistica Sinica.

CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data

Anne-Laure Boulesteix, Martin Daumer, Martin Slawski, 2008, BMC Bioinformatics.

Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics

Fernando Mut, Christopher M. Putman, Juan R. Cebral, 2018, International Journal of Computer Assisted Radiology and Surgery.

Quantized Random Projections and Non-Linear Estimation of Cosine Similarity

Ping Li, Michael Mitzenmacher, Martin Slawski, 2016, NIPS.

Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching

Matthias Hein, Andreas Hildebrandt, Andreas Tholey, 2012, BMC Bioinformatics.

The structured elastic net for quantile regression and support vector classification

Martin Slawski, M. Slawski, 2012, Stat. Comput..

Prediction-time Efficient Classification Using Feature Computational Dependencies

Liang Zhao, Amir Alipour-Fanid, Kai Zeng, 2018, KDD.

Matrix factorization with binary components

Matthias Hein, Pavlo Lutsik, Martin Slawski, 2013, NIPS.

Sparse recovery by thresholded non-negative least squares

Matthias Hein, Martin Slawski, Matthias Hein, 2011, NIPS.

Asynchronous Online Federated Learning for Edge Devices with Non-IID Data

Martin Slawski, Huzefa Rangwala, Yujing Chen, 2019, 2020 IEEE International Conference on Big Data (Big Data).

Linear signal recovery from b-bit-quantized linear measurements: precise analysis of the trade-off between bit depth and number of measurements pdf

Ping Li, Martin Slawski, Ping Li, 2016, ArXiv.

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

Ping Li, Martin Slawski, Ping Li, 2018, IEEE Transactions on Information Theory.

Large-scale Cost-Aware Classification Using Feature Computational Dependency Graph

Yanfang Ye, Amir Alipour-Fanid, Lingfei Wu, 2021, IEEE Transactions on Knowledge and Data Engineering.

Stability and aggregation of ranked gene lists

Anne-Laure Boulesteix, Martin Slawski, A. Boulesteix, 2009, Briefings Bioinform..

Permuted and Unlinked Monotone Regression in Rd: an approach based on mixture modeling and optimal transport pdf

Martin Slawski, Bodhisattva Sen, 2022, ArXiv.

Permuted and Unlinked Monotone Regression in $\mathbb{R}^d$: an approach based on mixture modeling and optimal transport

Martin Slawski, Bodhisattva Sen, B. Sen, 2022, 2201.03528.

Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group pdf

Zhenbang Wang, Martin Slawski, Emanuel Ben-David, 2021, AISTATS.

Regression with linked datasets subject to linkage error

Zhenbang Wang, Martin Slawski, Guoqing Diao, 2021 .

Robust sparse recovery with non-negativity constraints

Matthias Hein, Martin Slawski, 2011 .

Development of a statistical model for discrimination of rupture status in posterior communicating artery aneurysms

Fernando Mut, Michael Pritz, Christopher Putman, 2018, Acta Neurochirurgica.

Feature selection guided by structural information

Gerhard Tutz, Wolfgang zu Castell, Martin Slawski, 2010, 1011.2315.

MeDeCom: discovery and quantification of latent components of heterogeneous methylomes

Matthias Hein, Nikita Vedeneev, Jörn Walter, 2017, Genome Biology.

Asynchronous Online Federated Learning for Edge Devices with Non-IID Data

Huzefa Rangwala, Yue Ning, Martin Slawski, 2019, 2020 IEEE International Conference on Big Data (Big Data).

Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics.

Norman Juchler, Vartan Kurtcuoglu, Sven Hirsch, 2019, Neurosurgical focus.

On Principal Components Regression, Random Projections, and Column Subsampling

Martin Slawski, M. Slawski, 2017, 1709.08104.