Publications

@article{LearningTransitionOperators_1,

title = {Learning Transition Operators From Sparse Space-Time Samples},

author = {Christian Kuemmerle and Mauro Maggioni and Sui Tang},

url = {https://arxiv.org/abs/2212.00746

https://ieeexplore.ieee.org/document/10558780},

doi = {10.1109/TIT.2024.3413534},

year  = {2024},

date = {2024-06-14},

urldate = {2024-06-14},

journal = {IEEE Transactions on Information Theory},

abstract = {We consider the nonlinear inverse problem of learning a transition operator A from partial observations at different times, in particular from sparse observations of entries of its powers A,A2,⋯,AT. This Spatio-Temporal Transition Operator Recovery problem is motivated by the recent interest in learning time-varying graph signals that are driven by graph operators depending on the underlying graph topology. We address the nonlinearity of the problem by embedding it into a higher-dimensional space of suitable block-Hankel matrices, where it becomes a low-rank matrix completion problem, even if A is of full rank. For both a uniform and an adaptive random space-time sampling model, we quantify the recoverability of the transition operator via suitable measures of incoherence of these block-Hankel embedding matrices. For graph transition operators these measures of incoherence depend on the interplay between the dynamics and the graph topology. We develop a suitable non-convex iterative reweighted least squares (IRLS) algorithm, establish its quadratic local convergence, and show that, in optimal scenarios, no more than (rnlog(nT)) space-time samples are sufficient to ensure accurate recovery of a rank-r operator A of size n×n. This establishes that spatial samples can be substituted by a comparable number of space-time samples. We provide an efficient implementation of the proposed IRLS algorithm with space complexity of order O(rnT) and per-iteration time complexity linear in n. Numerical experiments for transition operators based on several graph models confirm that the theoretical findings accurately track empirical phase transitions, and illustrate the applicability and scalability of the proposed algorithm.},

keywords = {dynamical systems, Machine learning, optimization, sparsity, statistics},

pubstate = {published},

tppubtype = {article}

}

@article{MMS:NoisyDictionaryLearning,

title = {Multiscale Dictionary Learning: Non-asymptotic Bounds and Robustness},

author = {Mauro Maggioni and Stanislav Minsker and Nate Strawn},

url = {http://dl.acm.org/citation.cfm?id=2946645.2946647},

issn = {1532-4435},

year  = {2016},

date = {2016-01-01},

journal = {J. Mach. Learn. Res.},

volume = {17},

number = {1},

pages = {43--93},

publisher = {JMLR.org},

keywords = {dictionary learning, Manifold Learning, multi-resolution analysis, robustness, sparsity},

pubstate = {published},

tppubtype = {article}

}