Linear regression with partially mismatched data: local search with theoretical guarantees
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Publication:2689832
DOI10.1007/s10107-022-01863-yOpenAlexW3171982279WikidataQ114228477 ScholiaQ114228477MaRDI QIDQ2689832
Publication date: 14 March 2023
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2106.02175
polynomial time algorithmmixed integer programminglinear regressionlocal search methodpermutation learningmismatched data
Linear inference, regression (62J99) Large-scale problems in mathematical programming (90C06) Combinatorial optimization (90C27)
Cites Work
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- Linear regression with sparsely permuted data
- Linear regression with mismatched data: a provably optimal local search algorithm
- Signal Recovery From Unlabeled Samples
- Signal Amplitude Estimation and Detection From Unlabeled Binary Quantized Samples
- High-Dimensional Statistics
- A Pseudo-Likelihood Approach to Linear Regression With Partially Shuffled Data
- An Algebraic-Geometric Approach for Linear Regression Without Correspondences
- Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
- Unlabeled Sensing With Random Linear Measurements
- Linear Regression With Shuffled Data: Statistical and Computational Limits of Permutation Recovery
- Matchmaking
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