This is my full list of publications. For selected publications representative of my work, go here. Please also see my Google scholar profile; it is sometimes more up to date.
Preprints
Cavazos, J. S., Fessler, J. A., & Balzano, L. (2025). ALPCAHUS: Subspace Clustering for Heteroscedastic Data (No. arXiv:2505.18918). arXiv. https://doi.org/10.48550/arXiv.2505.18918
Yaras, C., Xu, A. S., Abillama, P., Lee, C., & Balzano, L. (2025). MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention (No. arXiv:2505.18698). arXiv. https://doi.org/10.48550/arXiv.2505.18698
Kwon, S. M., Xu, A. S., Yaras, C., Balzano, L., & Qu, Q. (2025). Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective (No. arXiv:2505.14808). arXiv. https://doi.org/10.48550/arXiv.2505.14808
Gilman, K., Hong, D., Fessler, J. A., & Balzano, L. (2025). Streaming Heteroscedastic Probabilistic PCA with Missing Data (No. arXiv:2310.06277). arXiv. https://doi.org/10.48550/arXiv.2310.06277
Balzano, L., Ding, T., Haeffele, B. D., Kwon, S. M., Qu, Q., Wang, P., Wang, Z., & Yaras, C. (2025). An Overview of Low-Rank Structures in the Training and Adaptation of Large Models (No. arXiv:2503.19859). arXiv. https://doi.org/10.48550/arXiv.2503.19859
Li, Y., Balzano, L., Needell, D., & Lyu, H. (2024). Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization (No. arXiv:2312.10330). arXiv. https://doi.org/10.48550/arXiv.2312.10330
Wang, P., Li, X., Yaras, C., Zhu, Z., Balzano, L., Hu, W., & Qu, Q. (2024). Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination (No. arXiv:2311.02960). arXiv. https://doi.org/10.48550/arXiv.2311.02960
Yaras, C., Wang, P., Hu, W., Zhu, Z., Balzano, L., & Qu, Q. (2023). The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks (No. arXiv:2306.01154). arXiv. https://doi.org/10.48550/arXiv.2306.01154
Blocker, C. J., Raja, H., Fessler, J. A., & Balzano, L. (2023). Dynamic Subspace Estimation with Grassmannian Geodesics (No. arXiv:2303.14851). arXiv. https://doi.org/10.48550/arXiv.2303.14851
Ritchie, A., Balzano, L., Kessler, D., Sripada, C. S., & Scott, C. (2022). Supervised PCA: A Multiobjective Approach (No. arXiv:2011.05309). arXiv. https://doi.org/10.48550/arXiv.2011.05309
Tarzanagh, D. A., Balzano, L., & Hero, A. O. (2021). Fair Structure Learning in Heterogeneous Graphical Models (No. arXiv:2112.05128). arXiv. https://doi.org/10.48550/arXiv.2112.05128
Sattar, Y., Du, Z., Tarzanagh, D. A., Balzano, L., Ozay, N., & Oymak, S. (2021). Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds (No. arXiv:2111.07018). arXiv. https://doi.org/10.48550/arXiv.2111.07018
Published
2025
Gilman, K., Burer, S., & Balzano, L. (2025). A Semidefinite Relaxation for Sums of Heterogeneous Quadratic Forms on the Stiefel Manifold. SIAM Journal on Matrix Analysis and Applications, 1091–1116. https://doi.org/10.1137/23M1545136
Wang, P., Jiang, R., Kong, Q., & Balzano, L. (2025). A Proximal Difference-of-Convex Algorithm for Sample Average Approximation of Chance Constrained Programming. INFORMS Journal on Computing. https://doi.org/10.1287/ijoc.2024.0648
Hong, D., & Balzano, L. (2025). Optimal sample acquisition for optimally weighted PCA from heterogeneous quality sources. IEEE Signal Processing Letters, 1–5. https://doi.org/10.1109/LSP.2025.3550280
Salazar Cavazos, J., Fessler, J. A., & Balzano, L. (2025). ALPCAH: Subspace Learning for Sample-Wise Heteroscedastic Data. IEEE Transactions on Signal Processing, 73, 876–886. https://doi.org/10.1109/TSP.2025.3537867
2024
Hume, J., & Balzano, L. (2024, November 19). A Spectral Framework for Tracking Communities in Evolving Networks. The Third Learning on Graphs Conference. https://openreview.net/forum?id=es9LIeVa9s
Yaras, C., Wang, P., Balzano, L., & Qu, Q. (2024, June 6). Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. Forty-first International Conference on Machine Learning. https://openreview.net/forum?id=uDkXoZMzBv
Liu, H., Wang, P., Huang, L., Qu, Q., & Balzano, L. (2024, June 6). Symmetric Matrix Completion with ReLU Sampling. Forty-first International Conference on Machine Learning. https://openreview.net/forum?id=VxI0gInNlh
Li, Y., Balzano, L., Needell, D., & Lyu, H. (2024, June 6). Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms. Forty-first International Conference on Machine Learning. https://openreview.net/forum?id=7KtFQnF368
Kwon, S. M., Zhang, Z., Song, D., Balzano, L., & Qu, Q. (2024). Efficient Low-Dimensional Compression of Overparameterized Models. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 1009–1017. https://proceedings.mlr.press/v238/min-kwon24a.html
Tarzanagh, D. A., Nazari, P., Hou, B., Shen, L., & Balzano, L. (2024). Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 2854–2862. https://proceedings.mlr.press/v238/ataee-tarzanagh24a.html
Geelen, R., Balzano, L., Wright, S., & Willcox, K. (2024). Learning physics-based reduced-order models from data using nonlinear manifolds. Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(3), 033122. https://doi.org/10.1063/5.0170105
Wang, P., Li, X., Yaras, C., Zhu, Z., Balzano, L., Hu, W., & Qu, Q. (2024, January 3). Understanding Hierarchical Representations in Deep Networks via Feature Compression and Discrimination. Conference on Parsimony and Learning (Recent Spotlight Track). https://openreview.net/forum?id=Ovuu8LpGZu
Kwon, S. M., Zhang, Z., Song, D., Balzano, L., & Qu, Q. (2024, January 3). Efficient Low-Dimensional Compression of Overparameterized Networks. Conference on Parsimony and Learning (Recent Spotlight Track). https://openreview.net/forum?id=1AVb9oEdK7
Yaras, C., Wang, P., Hu, W., Zhu, Z., Balzano, L., & Qu, Q. (2024, January 3). Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Linear Networks. Conference on Parsimony and Learning (Recent Spotlight Track). https://openreview.net/forum?id=oSzCKf1I5N
2023
Newton, R., Du, Z., Seiler, P., & Balzano, L. (2023). Optimality of POD for Data-Driven LQR With Low-Rank Structures. IEEE Control Systems Letters, 8, 85–90. https://doi.org/10.1109/LCSYS.2023.3344147
Geelen, R., Balzano, L., & Willcox, K. (2023). Learning Latent Representations in High-Dimensional State Spaces Using Polynomial Manifold Constructions. 2023 62nd IEEE Conference on Decision and Control (CDC), 4960–4965. https://doi.org/10.1109/CDC49753.2023.10384209
Yaras, C., Wang, P., Hu, W., Zhu, Z., Balzano, L., & Qu, Q. (2023, November 7). Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Matrix Factorizations. NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning. https://openreview.net/forum?id=4pPnQqUMLS
Newton, R., Du, Z., Balzano, L., & Seiler, P. (2023). Manifold Optimization for Data Driven Reduced-Order Modeling*. 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 1–6. https://doi.org/10.1109/Allerton58177.2023.10313500
Cavazos, J. S., Fessler, J. A., & Balzano, L. (2023). ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization. 2023 International Conference on Sampling Theory and Applications (SampTA), 1–6. https://doi.org/10.1109/SampTA59647.2023.10301206
Soleymani, M., Liu, Q., Mahdavifar, H., & Balzano, L. (2023). Matrix Completion over Finite Fields: Bounds and Belief Propagation Algorithms. 2023 IEEE International Symposium on Information Theory (ISIT), 1166–1171. https://doi.org/10.1109/ISIT54713.2023.10206551
Xu, A. S., Balzano, L., & Fessler, J. A. (2023). HeMPPCAT: Mixtures of Probabilistic Principal Component analysers for data with heteroscedastic noise. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. https://doi.org/10.1109/ICASSP49357.2023.10094719
Hong, D., Yang, F., Fessler, J. A., & Balzano, L. (2023). Optimally Weighted PCA for High-Dimensional Heteroscedastic Data. SIAM Journal on Mathematics of Data Science, 5(1), 222–250. https://doi.org/10.1137/22M1470244
2022
Wang, P., Liu, H., Yaras, C., Balzano, L., & Qu, Q. (2022, November 26). Linear Convergence Analysis of Neural Collapse with Unconstrained Features. OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop). https://openreview.net/forum?id=WC9im-M_y5
Naik, R., Trivedi, N., Tarzanagh, D. A., & Balzano, L. (2022). Truncated Matrix Completion - An Empirical Study. 2022 30th European Signal Processing Conference (EUSIPCO), 847–851. https://doi.org/10.23919/EUSIPCO55093.2022.9909952
Wang, P., Liu, H., So, A. M.-C., & Balzano, L. (2022). Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering. Proceedings of the 39th International Conference on Machine Learning, 22884–22918. https://proceedings.mlr.press/v162/wang22r.html
Sattar, Y., Du, Z., Tarzanagh, D. A., Oymak, S., Balzano, L., & Ozay, N. (2022). Certainty Equivalent Quadratic Control for Markov Jump Systems. 2022 American Control Conference (ACC), 2871–2878. https://doi.org/10.23919/ACC53348.2022.9867208
Du, Z., Sattar, Y., Tarzanagh, D. A., Balzano, L., Ozay, N., & Oymak, S. (2022). Data-Driven Control of Markov Jump Systems: Sample Complexity and Regret Bounds. 2022 American Control Conference (ACC), 4901–4908. https://doi.org/10.23919/ACC53348.2022.9867863
Du, Z., Ozay, N., & Balzano, L. (2022). Clustering-based Mode Reduction for Markov Jump Systems. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 689–701. https://proceedings.mlr.press/v168/du22a.html
Balzano, L. (2022). On the equivalence of Oja’s algorithm and GROUSE. Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, 7014–7030. https://proceedings.mlr.press/v151/balzano22a.html
Zhang, D., & Balzano, L. (2022). Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation From Undersampled Data. University of Michigan Technical Report. https://doi.org/10.7302/4151
Yaras, C., Wang, P., Zhu, Z., Balzano, L., & Qu, Q. (2022). Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. Advances in Neural Information Processing Systems. https://openreview.net/forum?id=Zvh6lF5b26N
Du, Z., Balzano, L., & Ozay, N. (2022). Mode Reduction for Markov Jump Systems. IEEE Open Journal of Control Systems, 1–19. https://doi.org/10.1109/OJCSYS.2022.3212613
Gilman, K., Tarzanagh, D. A., & Balzano, L. (2022). Grassmannian Optimization for Online Tensor Completion and Tracking With the t-SVD. IEEE Transactions on Signal Processing, 70, 2152–2167. https://doi.org/10.1109/TSP.2022.3164837
2021
Lipor, J., Hong, D., Tan, Y. S., & Balzano, L. (2021). Subspace clustering using ensembles of K-subspaces. Information and Inference: A Journal of the IMA, 10(1), 73–107. https://doi.org/10.1093/imaiai/iaaa031
Ongie, G., Pimentel-Alarcón, D., Balzano, L., Willett, R., & Nowak, R. D. (2021). Tensor Methods for Nonlinear Matrix Completion. SIAM Journal on Mathematics of Data Science, 253–279. https://doi.org/10.1137/20M1323448
Hong, D., Gilman, K., Balzano, L., & Fessler, J. A. (2021). HePPCAT: Probabilistic PCA for Data With Heteroscedastic Noise. IEEE Transactions on Signal Processing, 69, 4819–4834. https://doi.org/10.1109/TSP.2021.3104979
2020
Bower, A., & Balzano, L. (2020). Preference Modeling with Context-Dependent Salient Features. International Conference on Machine Learning, 1067–1077. https://proceedings.mlr.press/v119/bower20a.html
Gilman, K., & Balzano, L. (2020). Online Tensor Completion and Free Submodule Tracking With The T-SVD. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3282–3286. https://doi.org/10.1109/ICASSP40776.2020.9053199
Lipor, J., & Balzano, L. (2020). Clustering quality metrics for subspace clustering. Pattern Recognition, 107328. https://doi.org/10.1016/j.patcog.2020.107328
Lyu, H., Needell, D., & Balzano, L. (2020). Online matrix factorization for Markovian data and applications to Network Dictionary Learning. Journal of Machine Learning Research, 21(251), 1–49. http://jmlr.org/papers/v21/20-444.html
Thong, T., Wang, Y., Brooks, M. D., Lee, C. T., Scott, C., Balzano, L., Wicha, M. S., & Colacino, J. A. (2020). Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics. Frontiers in Cell and Developmental Biology, 8. https://doi.org/10.3389/fcell.2020.00288
2019
Du, Z., Ozay, N., & Balzano, L. (2019). Mode Clustering for Markov Jump Systems. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 126–130. https://doi.org/10.1109/CAMSAP45676.2019.9022650
Hong, D., Balzano, L., & Fessler, J. A. (2019). Probabilistic PCA for Heteroscedastic Data. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 26–30. https://doi.org/10.1109/CAMSAP45676.2019.9022436
Hong, D., Lei, S., Mathieu, J. L., & Balzano, L. (2019). Exploration of tensor decomposition applied to commercial building baseline estimation. 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 1–5. https://doi.org/10.1109/GlobalSIP45357.2019.8969417
Ritchie, A., Scott, C., Balzano, L., Kessler, D., & Sripada, C. S. (2019). Supervised Principal Component Analysis Via Manifold Optimization. 2019 IEEE Data Science Workshop (DSW), 6–10. https://doi.org/10.1109/DSW.2019.8755587
Wang, Y., Thong, T., Saligrama, V., Colacino, J., Balzano, L., & Scott, C. (2019). A gene filter for comparative analysis of single-cell RNA-sequencing trajectory datasets. BioRxiv, 637488. https://doi.org/10.1101/637488
Eftekhari, A., Ongie, G., Balzano, L., & Wakin, M. B. (2019). Streaming Principal Component Analysis From Incomplete Data. Journal of Machine Learning Research, 20(86), 1–62. http://jmlr.org/papers/v20/16-627.html
Gilman, K., & Balzano, L. (2019). Panoramic Video Separation with Online Grassmannian Robust Subspace Estimation. Proceedings of the IEEE International Conference on Computer Vision Workshops. Proceedings of the IEEE International Conference on Computer Vision Workshops. http://openaccess.thecvf.com/content_ICCVW_2019/html/RSL-CV/Gilman_Panoramic_Video_Separation_with_Online_Grassmannian_Robust_Subspace_Estimation_ICCVW_2019_paper.html
2018
Gitlin, A., Tao, B., Balzano, L., & Lipor, J. (2018). Improving $K$-Subspaces via Coherence Pursuit. IEEE Journal of Selected Topics in Signal Processing, 12(6), 1575–1588. https://doi.org/10.1109/JSTSP.2018.2869363
Zhang, D., Zhao, T., & Balzano, L. (2018). INFORMATION MAXIMIZATION AUTO-ENCODING. https://openreview.net/forum?id=SyVpB2RqFX
Hong, D., Balzano, L., & Fessler, J. A. (2018). Asymptotic performance of PCA for high-dimensional heteroscedastic data. Journal of Multivariate Analysis, 167, 435–452. https://doi.org/10.1016/j.jmva.2018.06.002
Hong, D., Malinas, R. P., Fessler, J. A., & Balzano, L. (2018). Learning Dictionary-Based Unions of Subspaces for Image Denoising. 2018 26th European Signal Processing Conference (EUSIPCO), 1597–1601. https://doi.org/10.23919/EUSIPCO.2018.8553117
Ledva, G. S., Balzano, L., & Mathieu, J. L. (2018). Exploring Connections Between a Multiple Model Kalman Filter and Dynamic Fixed Share with Applications to Demand Response. 2018 IEEE Conference on Control Technology and Applications (CCTA), 217–223. https://doi.org/10.1109/CCTA.2018.8511493
Ongie, G., Hong, D., Zhang, D., & Balzano, L. (2018). Online Estimation of Coherent Subspaces with Adaptive Sampling. 2018 IEEE Statistical Signal Processing Workshop (SSP), 841–845. https://doi.org/10.1109/SSP.2018.8450830
Zhang, D., Katz-Samuels, J., Figueiredo, M. A. T., & Balzano, L. (2018). Simultaneous Sparsity and Parameter Tying for Deep Learning Using Ordered Weighted ℓ1 Regularization. 2018 IEEE Statistical Signal Processing Workshop (SSP), 65–69. https://doi.org/10.1109/SSP.2018.8450819
Zhang, D., Wang, H., Figueiredo, M., & Balzano, L. (2018). Learning to Share: Simultaneous Parameter Tying and Sparsification in Deep Learning. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=rypT3fb0b
Bower, A., Jain, L., & Balzano, L. (2018). The Landscape of Non-Convex Quadratic Feasibility. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3974–3978. https://doi.org/10.1109/ICASSP.2018.8461868
Du, Z., Balzano, L., & Ozay, N. (2018). A Robust Algorithm for Online Switched System Identification. IFAC-PapersOnLine, 51(15), 293–298. https://doi.org/10.1016/j.ifacol.2018.09.150
Ledva, G. S., Balzano, L., & Mathieu, J. L. (2018). Real-Time Energy Disaggregation of a Distribution Feeder’s Demand Using Online Learning. IEEE Transactions on Power Systems, 1–1. https://doi.org/10.1109/TPWRS.2018.2800535
Balzano, L., Chi, Y., & Lu, Y. M. (2018). Streaming PCA and Subspace Tracking: The Missing Data Case. Proceedings of the IEEE, 1–18. https://doi.org/10.1109/JPROC.2018.2847041
Ledva, G. S., Du, Z., Balzano, L., & Mathieu, J. L. (2018). Disaggregating Load by Type from Distribution System Measurements in Real Time. In S. Meyn, T. Samad, I. Hiskens, & J. Stoustrup (Eds.), Energy Markets and Responsive Grids (Vol. 162, pp. 413–437). Springer New York. https://doi.org/10.1007/978-1-4939-7822-9_17
Ongie, G., Murthy, N., Balzano, L., & Fessler, J. A. (2018). A Memory-efficient Algorithm for Large-scale Sparsity Regularized Image Reconstruction. Proceedings of the International Conference on Image Formation in X-Ray Computed Tomography. http://arxiv.org/abs/1904.00423
2017
Zhang, D., Sun, Y., Eriksson, B., & Balzano, L. (2017). Deep Unsupervised Clustering Using Mixture of Autoencoders. ArXiv:1712.07788 [Cs, Stat]. http://arxiv.org/abs/1712.07788
Ongie, G., Dewangan, S., Fessler, J. A., & Balzano, L. (2017). Online dynamic MRI reconstruction via robust subspace tracking. 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 1180–1184. https://doi.org/10.1109/GlobalSIP.2017.8309147
Lipor, J., Wong, B. P., Scavia, D., Kerkez, B., & Balzano, L. (2017). Distance-Penalized Active Learning Using Quantile Search. IEEE Transactions on Signal Processing, 65(20), 5453–5465. https://doi.org/10.1109/TSP.2017.2731323
Pimentel-Alarcón, D., Ongie, G., Balzano, L., Willett, R., & Nowak, R. (2017). Low algebraic dimension matrix completion. 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 790–797. https://doi.org/10.1109/ALLERTON.2017.8262820
Ongie, G., Willett, R., Nowak, R. D., & Balzano, L. (2017). Algebraic Variety Models for High-Rank Matrix Completion. Proceedings of the 34th International Conference on Machine Learning, 2691–2700. https://proceedings.mlr.press/v70/ongie17a.html
Lipor, J., & Balzano, L. (2017). Leveraging Union of Subspace Structure to Improve Constrained Clustering. Proceedings of the 34th International Conference on Machine Learning, 2130–2139. https://proceedings.mlr.press/v70/lipor17a.html
Pimentel-Alarcón, D., Balzano, L., Marcia, R., Nowak, R., & Willett, R. (2017). Mixture regression as subspace clustering. 2017 International Conference on Sampling Theory and Applications (SampTA), 456–459. https://doi.org/10.1109/SAMPTA.2017.8024386
Eftekhari, A., Balzano, L., & Wakin, M. B. (2017). What to Expect When You Are Expecting on the Grassmannian. IEEE Signal Processing Letters, 24(6), 872–876. https://doi.org/10.1109/LSP.2017.2684784
Zhang, D., & Balzano, L. (2017). Matched subspace detection using compressively sampled data. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4601–4605. https://doi.org/10.1109/ICASSP.2017.7953028
Ganti, R., Rao, N., Balzano, L., Willett, R., & Nowak, R. (2017, February 13). On Learning High Dimensional Structured Single Index Models. Thirty-First AAAI Conference on Artificial Intelligence. Thirty-First AAAI Conference on Artificial Intelligence. https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14480
Ongie, G., Hong, D., Zhang, D., & Balzano, L. (2017). Enhanced Online Subspace Estimation via Adaptive Sensing. Asilomar Confernce on Signals, Systems, and Computers. Asilomar Confernce on Signals, Systems, and Computers. https://pdfs.semanticscholar.org/ba2f/61c45e92ae471552d55a8350f7211b02e6b0.pdf
2016
Kennedy, R., Balzano, L., Wright, S. J., & Taylor, C. J. (2016). Online algorithms for factorization-based structure from motion. Computer Vision and Image Understanding, 150, 139–152. https://doi.org/10.1016/j.cviu.2016.04.011
Hong, D., Balzano, L., & Fessler, J. A. (2016). Towards a theoretical analysis of PCA for heteroscedastic data. 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 496–503. https://doi.org/10.1109/ALLERTON.2016.7852272
Xiao, P., & Balzano, L. (2016). Online sparse and orthogonal subspace estimation from partial information. 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 284–291. https://doi.org/10.1109/ALLERTON.2016.7852242
Pimentel-Alarcón, D., Balzano, L., Marcia, R., Nowak, R., & Willett, R. (2016). Group-sparse subspace clustering with missing data. 2016 IEEE Statistical Signal Processing Workshop (SSP), 1–5. https://doi.org/10.1109/SSP.2016.7551734
Pimentel-Alarcón, D., Balzano, L., & Nowak, R. (2016). Necessary and sufficient conditions for sketched subspace clustering. Allerton Conference on Communication, Control, and Computing. https://danielpimentel.github.io/pdfs/sketchedSC.pdf
Zhang, D., & Balzano, L. (2016). Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 1460–1468. http://jmlr.org/proceedings/papers/v51/zhang16b.html
2015
Lipor, J., & Balzano, L. (2015). Margin-based active subspace clustering. 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 377–380. https://doi.org/10.1109/CAMSAP.2015.7383815
Ledva, G., Balzano, L., & Mathieu, J. (2015, September). Inferring the behavior of distributed energy resources with online learning. Proceedings of the Allerton Conference on Communication, Control, and Computing.
Lipor, J., Balzano, L., Kerkez, B., & Scavia, D. (2015). Quantile search: A distance-penalized active learning algorithm for spatial sampling. 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 1241–1248. https://doi.org/10.1109/ALLERTON.2015.7447150
Ganti, R. S., Balzano, L., & Willett, R. (2015). Matrix Completion Under Monotonic Single Index Models. Proceedings of the Conference for Advances in Neural Information Processing Systems, 1864–1872. http://papers.nips.cc/paper/5916-matrix-completion-under-monotonic-single-index-models
2014
Kennedy, R., Taylor, C. J., & Balzano, L. (2014). Online completion of Ill-conditioned low-rank matrices. 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 507–511. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7032169
Balzano, L., & Wright, S. J. (2014). Local Convergence of an Algorithm for Subspace Identification from Partial Data. Foundations of Computational Mathematics, 1–36. http://link.springer.com/article/10.1007/s10208-014-9227-7
He, J., Zhang, D., Balzano, L., & Tao, T. (2014). Iterative Grassmannian optimization for robust image alignment. Image and Vision Computing, 32(10), 800–813. http://www.sciencedirect.com/science/article/pii/S0262885614000523
Pimentel, D., Nowak, R., & Balzano, L. (2014). On the sample complexity of subspace clustering with missing data. 2014 IEEE Workshop on Statistical Signal Processing (SSP), 280–283. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6884630
Lipor, J., & Balzano, L. (2014). Robust blind calibration via total least squares. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4244–4248. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6854402&tag=1
Kennedy, R., Balzano, L., Wright, S. J., & Taylor, C. J. (2014). Online algorithms for factorization-based structure from motion. 2014 IEEE Winter Conference on Applications of Computer Vision (WACV), 37–44. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6836120
Brown, S. G., Russell-Graham, A., Xiao, P., & Balzano, L. (2014). Determination of Trends in Ozone in the Mid-Atlantic Using Non-Negative Matrix Factorization. AGU Fall Meeting Abstracts. http://adsabs.harvard.edu/abs/2014AGUFM.A23E3307B
Jun He, Laura Balzano, & Arthur Szlam. (2014). Online Robust Background Modeling via Alternating Grassmannian Optimization. In Background Modeling and Foreground Detection for Video Surveillance (Vol. 1–0, pp. 16-1-16–26). Chapman and Hall/CRC. http://dx.doi.org/10.1201/b17223-24
2013
Balzano, L., & Wright, S. J. (2013). On GROUSE and incremental SVD. 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 1–4. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6713992
He, J., Zhang, D., Balzano, L., & Tao, T. (2013, April). Iterative Online Subspace Learning for Robust Image Alignment. Proceedings of the IEEE Conference on Face and Gesture Recognition. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6553759
2012
He, J., Balzano, L., & Szlam, A. (2012). Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference On, 1568–1575. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6247848
Tan, V. Y., Balzano, L., & Draper, S. C. (2012). Rank minimization over finite fields: Fundamental limits and coding-theoretic interpretations. Information Theory, IEEE Transactions On, 58(4), 2018–2039. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6094216
Eriksson, B., Balzano, L., & Nowak, R. (2012). High rank matrix completion. Proc. of Intl. Conf. on Artificial Intell. and Stat. http://jmlr.csail.mit.edu/proceedings/papers/v22/eriksson12/eriksson12.pdf 1
Balzano, L., Szlam, A., Recht, B., & Nowak, R. (2012). K-subspaces with missing data. Statistical Signal Processing Workshop (SSP), 2012 IEEE, 612–615. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6319774
2011
Balzano, L., Nowak, R., & Roughan, M. (2011). On the success of network inference using a markov routing model. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3108–3111. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5946353
Tan, V. Y., Balzano, L., & Draper, S. C. (2011). Rank minimization over finite fields. Information Theory Proceedings (ISIT), 2011 IEEE International Symposium On, 1195–1199. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6033722
2010
Balzano, L., Nowak, R., & Recht, B. (2010). Online identification and tracking of subspaces from highly incomplete information. Communication, Control, and Computing (Allerton), 2010 48th Annual Allerton Conference On, 704–711. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5706976
Balzano, L., Recht, B., & Nowak, R. (2010). High-dimensional matched subspace detection when data are missing. Information Theory Proceedings (ISIT), 2010 IEEE International Symposium On, 1638–1642. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5513344
2009
Ni, K., Ramanathan, N., Chehade, M. N. H., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., & Srivastava, M. (2009). Sensor network data fault types. ACM Transactions on Sensor Networks (TOSN), 5(3), 25. http://dl.acm.org/citation.cfm?id=1525863
2008
Balzano, L., & Nowak, R. (2008). Blind Calibration of Networks of Sensors: Theory and Algorithms. In V. Saligrama (Ed.), Networked Sensing Information and Control (pp. 9–37). Springer US. http://link.springer.com.proxy.lib.umich.edu/chapter/10.1007/978-0-387-68845-9_1
Ganeriwal, S., Balzano, L. K., & Srivastava, M. B. (2008). Reputation-based framework for high integrity sensor networks. ACM Transactions on Sensor Networks (TOSN), 4(3), 15. http://dl.acm.org/citation.cfm?id=1362546
2007
Balzano, L., & Nowak, R. (2007). Blind calibration of sensor networks. Proceedings of the 6th International Conference on Information Processing in Sensor Networks, 79–88. http://dl.acm.org/citation.cfm?id=1236372
2004
Gambiroza, V., Yuan, P., Balzano, L., Liu, Y., Sheafor, S., & Knightly, E. (2004). Design, analysis, and implementation of DVSR: a fair high-performance protocol for packet rings. Networking, IEEE/ACM Transactions On, 12(1), 85–102. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1268081
Thesis
Laura Balzano, Handling Missing Data in High-Dimensional Subspace Modeling, May 2012.
