Publications and Preprints of Gilad Lerman (with supplemental code)

Some Recent Preprints

S. K. Tamang, A. Ebtehaj, P. J. Van Leeuwen, D. Zou and G. Lerman. Ensemble Riemannian Data Assimilation over the Wasserstein Space. URL: arXiv:2009.03443

A. Gutierrez, S. Stewart and G. Lerman. You Need to Calm Down: Calmness Regularity for a Class of Seminorm Optimization Problems. URL: arXiv:2007.05689

C. Lai, D. Zou and G. Lerman. Novelty Detection via Robust Variational Autoencoding. URL: arXiv:2006.05534

T. Maunu and G. Lerman. A Provably Robust Multiple Rotation Averaging Scheme for SO(2). URL: arXiv:2002.05299

G. Lerman and Y. Shi. Robust Group Synchronization via Cycle-Edge Message Passing. URL: arXiv:1912.11347

T. Maunu and G. Lerman, Robust Subspace Recovery with Adversarial Outliers, URL: arXiv:1904.03275

M. Flores Rios, J. Calder, G. Lerman, Algorithms for lp-based semi-supervised learning on graphs, URL: arXiv:1901.05031

W.-K. Chen, M. Handschy, G. Lerman, Phase transition in random tensors with multiple spikes, URL: arXiv:1809.06790

X. Wang, K. Slavakis, and G. Lerman. Riemannian multi-manifold modeling. URL: arXiv:1410.0095. Link to supplementary webpage with code

X. Wang and G. Lerman. Nonparametric Bayesian regression on manifolds via Brownian motion. URL: arXiv:1507.06710. Link to suplemental webpage with code


Journal Publications

V. Huroyan, G. Lerman and H.-T. Wu, Solving jigsaw puzzles by the graph connection Laplacian, To appear in SIAM Journal on Imaging Sciences, 2020, URL: arXiv:1811.03188. Link to suplemental webpage with code

S. Tamang, A. Ebtehaj, D. Zou and G. Lerman. Regularized variational data assimilation for bias treatment using the Wasserstein metric. Quarterly Journal of the Royal Meteorological Society, 146 (2020), no. 730, 2332-2346, DOI: 10.1002/qj.3794

D. Zou and G. Lerman. Graph convolutional neural networks via scattering. Applied and Computational Harmonic Analysis, 49 (2020), no. 3, 1046-1074, DOI: 10.1016/j.acha.2019.06.003, earlier arxiv version: arXiv:1804.00099. Link to supplemental code

J. Goes, G. Lerman and B. Nadler. Robust sparse covariance estimation by thresholding Tyler's M-Estimator. 48 (2020), no. 1, 86-110. DOI: 10.1214/18-AOS1793, arxiv version: arXiv:1706.08020, Link to supplemental code

T. Maunu, T. Zhang and G. Lerman. A well-tempered landscape for non-convex robust subspace recovery. Journal of Machine Learning Research, 20 (2019), no. 37 (Feb.), URL: jmlr.org/papers/v20/17-324.html. Link to Supplemental code

G. Lerman, Y. Shi and T. Zhang, Exact camera location recovery by least unsquared deviations. SIAM Journal on Imaging Sciences, 11 (2018), no. 4, 2692-2721, DOI: 10.1137/17M115061X

V. Huroyan and G. Lerman. Distributed robust subspace recovery. SIAM Journal on Scientific Computing, 40 (2018), no. 5, A3067-A3090. DOI: 10.1137/17M1131659. Link to Supplemental code

G. Lerman and T. Maunu. An overview of robust subspace recovery. Proceedings of the IEEE, 106 (2018), no. 8, 1380-1410, DOI: 10.1109/JPROC.2018.2853141. Link to Supplemental code

G. Lerman and T. Maunu. Fast, robust and nonconvex subspace recovery. Information and Inference: A Journal of the IMA, 7 (2018), no. 2, 277-336, DOI: doi.org/10.1093/imaiai/iax012. Arxiv version arXiv:1406.6145 appeared in 2014. Link to supplemental code

W.-K. Chen, M. Handschy, G. Lerman. On the energy landscape of the mixed even p-spin model. Probability Theory and Related Fields, 171 (2018), no. 1-2. 53-95. DOI: 10.1007/s00440-017-0773-1. Free Arxiv version: arxiv:1609.04368

E. Arias-Castro, G. Lerman and T. Zhang. Spectral clustering based on local PCA. Journal of Machine Learning Research 18 (2017), no. 9, 1-57, freely available at JMLR. Link to supplemental code

Bryan Poling, Gilad Lerman, Enhancing Feature Tracking with Gyro Regularization. Image and Vision Computing (2016), Editor’s Choice Article, DOI: 10.1016/j.imavis.2016.01.004. Free Arxiv version: arxiv:1511.01508. Link to supplemental webpage with code

A. M. Ebtehaj, E. Foufoula-Georgiou, G. Lerman, and R. L. Bras. Compressive earth observatory: An insight from AIRS/AMSU retrievals. Geophysical Research Letters. 42 (2015), no. 2, 362-369, DOI: 10.1002/2014GL062711 (open access). Link to supplemental code

G. Lerman, M. McCoy, J. A. Tropp and T. Zhang. Robust computation of linear models by convex relaxation. Foundations of Computational Mathematics, 15 (2015), no. 2, 363-410, DOI: 10.1007/s10208-014-9221-0. Free Arxiv version: arxiv:1202.4044. Link to supplemental code

G. Lerman and T. Zhang. lp-Recovery of the most significant subspace among multiple subspaces with outliers. Constructive Approximation. 40 (2014), no. 3, 329–385, DOI: 10.1007/s00365-014-9242-6. Free Arxiv version: arxiv:1012.4116

B. Poling and G. Lerman. A new approach to two-view motion segmentation using global dimension minimization. International Journal of Computer Vision, 108 (2014), no. 3, 165-185, DOI: 10.1007/s11263-013-0694-0, Free Arxiv version: arxiv:1304.2999. Link to supplemental webpage with code and slides

T. Zhang and G. Lerman. A novel M-Estimator for robust PCA. Journal of Machine Learning Research 15 (2014), 749-808, freely available at JMLR.. Link to supplemental code

A. M. Ebtehaj, M. Zupanski, G. Lerman and E. Foufoula-Georgiou. Variational data assimilation via sparse regularisation. Tellus A, 66, (2014), no. 21789 1-17, DOI: dx.doi.org/10.3402/tellusa.v66.21789 (open access).

Y. Wang, A. Szlam and G. Lerman. Robust locally linear analysis with applications to image denoising and blind inpainting. SIAM Journal on Imaging Sciences, 6 (2013), no. 1, p. 526–562, DOI: 10.1137/110843642; see also freely downloadable version. Link to supplemental webpage with code

T. Zhang, A. Szlam, Y. Wang and G. Lerman. Hybrid linear modeling via local best-fit flats. International Journal of Computer Vision, 100 (2012), no. 3, p. 217-240, DOI: 10.1007/s11263-012-0535-6 (open access). Link to supplemental webpage with code

A. M. Ebtehaj, E. Foufoula-Georgiou and G. Lerman. Sparse regularization for precipitation downscaling. J. Geophys. Res., 117 (2012), D08107, p. 1-12, DOI:10.1029/2011JD017057 (open access). Link to supplemental code

G. Lerman and J. T. Whitehouse. Least squares approximations of measures via geometric condition numbers. Mathematika, 58 (2012), no. 1, p. 45-70, DOI: 10.1112/S0025579311001720. Free Arxiv version: arxiv:1008.2041

G. Lerman and T. Zhang. Robust recovery of multiple subspaces by geometric lp minimization. Annals of Statistics, 39 (2011), no. 5, 2686-2715, DOI: 10.1214/11-AOS914 (open access).

E. Arias-Castro, G. Chen and G. Lerman. Spectral clustering based on local linear approximations. Electronic Journal of Statistics, 5 (2011), no. 0, 1537-1587, DOI: 10.1214/11-EJS651 (open access). Link to supplemental webpage with code

G. Lerman and J. T. Whitehouse. High-dimensional Menger-type curvatures - part I: geometric multipoles and multiscale inequalities. Revista Matemática Iberoamericana, 27 (2011), no. 2, 493-555, DOI: 10.4171/RMI/645. Free Arxiv version: arxiv:0805.1425

G. Lerman and J. T. Whitehouse. High-dimensional Menger-type curvatures-part II: d-separation and a menagerie of curvatures. Constructive Approximation, 30 (2009), no. 3, 325-360. DOI: 10.1007/s00365-009-9073-z. Free Arxiv version: arxiv:0809.0137

G. Chen and G. Lerman. Foundations of a multi-way spectral clustering framework for hybrid linear modeling. Foundations of Computational Mathematics, 9 (2009), no. 5, 517-558. DOI:  10.1007/s10208-009-9043-7. Free Arxiv version: arxiv:0810.3724

G. Chen and G. Lerman. Spectral Curvature Clustering (SCC). International Journal of Computer Vision, 91 (2009), no. 81, 317-330. DOI: 10.1007/s11263-008-0178-9 (open access). Link to supplemental webpage with code

G. Lerman and J. T. Whitehouse. On d-dimensional d-semimetrics and simplex-type inequalities for high-dimensional sine functions. Journal of Approximation Theory, 156 (2009), no. 1, 52-81. DOI: 10.1016/j.jat.2008.03.005 (open access)

G. Lerman and B. Shakhnovich. Defining functional distance using manifold embeddings of gene ontology annotations. Proceedings of the National Academy of Sciences, 104 (2007), no. 27, 11334-11339. DOI: 10.1073/pnas.0702965104 (open access). Link to supplemental webpage with code

G. Lerman, J. McQuown, A. Blais, B. D. Dynlacht, G. Chen and B. Mishra. Functional genomics via multiscale analysis: Application to gene expression and ChIP-on-chip data. Bioinformatics, 23 (2007), no. 3, 314-320. DOI: 10.1093/bioinformatics/btl606 (open access). Link to supplemental webpage with code

G. Lerman. Quantifying curvelike structures of measures by L2 Jones quantities. Communications on Pure and Applied Mathematics, 56 (2003), no. 9, 1294-1365. DOI: 10.1002/cpa.10096 (open access), see also freely avaible version

G. Lerman and Z. Schuss, Asymptotic theory of large deviations for Markov chains. SIAM Journal on Applied Mathematics, 58 (1998), no. 6, 1862-1877. DOI: 10.1137/S003613999630051, see also freely downloadable version.

Refereed Conference Proceeding

Y. Shi, S. Li and G. Lerman. Robust multi-object matching via iterative reweighting of the graph connection Laplacian. To appear in the Proceedings of Neural Information processing Systems (NeurIPS), 2020, URL: arXiv:2006.06658

Y. Shi and G. Lerman. Message passing least squares framework and its application to rotation synchronization. To appear in the proceedings of the International Conference on Machine Learning (ICML), 2020, URL: arXiv:2007.13638, , Link to supplemental code

C.-H. Lai, D. Zou and G. Lerman. Robust subspace recovery layer for unsupervised anomaly detection. Online proceedings of the Eighth International Conference on Learning Representations (ICLR 2020). URL: openreview:rylb3eBtwr. Link to supplemental code

D. Zou and Gilad Lerman. Encoding robust representation for graph generation, Proceedings of the 2019 International Conference on Neural Networks (IJCNN 2019). pp. 1-9, DOI:10.1109/IJCNN.2019.8851705. Link to supplemental code

Y. Shi, G. Lerman. Estimation of camera locations in highly corrupted scenarios: All about that base, no shape trouble. Proceedings of Computer Vision and Pattern Recognition (CVPR), 2018, pp. 2868-2876, DOI: 10.1109/CVPR.2018.00303, Link to supplemental code:

X. Wang, K. Slavakis, and G. Lerman. Multi-manifold modeling in non-Euclidean spaces. Proceedings of the 8th International Conference on Artificial Intelligence and Statistics (AISTATS), 2015, JMLR W&CP 38 (2015), pp. 1023–1032. URL: click here. Link to supplementary webpage with code

B. Poling, G. Lerman and A. Szlam. Better Feature Tracking Through Subspace Constraints. To appear in the Proceedings of Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014. URL: click here. Link to supplemental webpage with code

J. Goes, T. Zhang, R. Arora and G. Lerman. Robust stochastic principal component analysis. Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), Reykjavik, Iceland, April 2014. JMLR W&CP 33 (2014): 266–274. URL: click here. Link to supplemental code

X. Wang, S. Atev, J. Wright and G. Lerman. Fast subspace search via Grassmannian based hashing. Proceedings of the International Conference of Computer Vision (ICCV), Sydney, Australia, 2013, pp. 2776-2783. URL: click here. Link to supplemental webpage with code

A. M. Ebtehaj, G. Lerman and E. Foufoula-Georgiou. Combined Radar-Radiometer Rainfall Retrieval via Sparse Representation. The Third International Workshop on Climate Informatics, NCAR, Boulder, CO, September 2013. URL: click here

M. Coudron and G. Lerman. On the sample complexity of robust PCA. Proceedings of Neural Information Processing Systems (NIPS), 2012, pp. 3230-3238, URL: click here

T. Zhang, A. Szlam, Y. Wang and G. Lerman. Randomized hybrid linear modeling by local best-fit flats. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 13-18 June 2010, pp. 1927-1934. DOI: 10.1109/CVPR.2010.5539866, freely available arxiv version. Link to supplemental webpage with code

G. Chen, S. Atev and G. Lerman. Kernel spectral curvature clustering (KSCC). Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, Sept. 27 - Oct. 4, 2009, pp. 765 - 772 (Best paper workshop award). DOI: 10.1109/ICCVW.2009.5457627, freely available arxiv version. Link to supplemental webpage with code

G. Chen and G. Lerman. Motion segmentation by SCC on the Hopkins 155 database. Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, Sept. 27 - Oct. 4, 2009, pp. 759 - 764. DOI: 10.1109/ICCVW.2009.5457626, freely available arxiv version. Link to supplemental webpage with code

T. Zhang, A. Szlam and G. Lerman. Median K-flats for hybrid linear modeling with many outliers. 2009 IEEE 12th International Conference on, Sept. 27 - Oct. 4, 2009, pp. 234 – 241. DOI: 10.1109/ICCVW.2009.5457695, freely available arxiv version. Link to supplemental code

G. Lerman. How to partition a low-dimensional data set into disjoint clusters of different geometric structures. Workshop on clustering high dimensional data and its applications at SIAM Data Mining, Arlington, VA, 2002. URL: click here

Unpublished Reports

G. Lerman, J. McQuown and B. Mishra. Multiscale robust regression, multiscale influence analysis and application to edge detection. URL: ~lerman/reports/msc_theory.pdf (last version from Aug. 2009)

G. Chen, G. Lerman and R. Chartrand. Multiscale analysis for muon-scattering data. Technical Report LA-UR 06-7504 (2006), Los Alamos National Laboratory. URL: ~lerman/reports/muon_lanl.pdf

M. Bern et al., Emerging challenges in computational topology, arXiv:cs/9909001v1 (1999)

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Last Modified Saturday October 03, 2020
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