Publications

@article{YYM:ATLAS2,

title = {Nonlinear model reduction for slow-fast stochastic systems near manifolds},

author = {Felix X.-F. Ye and Sichen Yang and Mauro Maggioni},

url = {https://arxiv.org/abs/2104.02120v1},

doi = {https://doi.org/10.1007/s43670-023-00055-9},

year  = {2023},

date = {2023-06-13},

urldate = {2023-11-04},

journal = {J Nonlinear Sci},

volume = {34},

number = {22},

issue = {1},

abstract = {We introduce a nonlinear stochastic model reduction technique for high-dimensional stochastic dynamical systems that have a low-dimensional invariant effective manifold with slow dynamics, and high-dimensional, large fast modes. Given only access to a black box simulator from which short bursts of simulation can be obtained, we estimate the invariant manifold, a process of the effective (stochastic) dynamics on it, and construct an efficient simulator thereof. These estimation steps can be performed on-the-fly, leading to efficient exploration of the effective state space, without losing consistency with the underlying dynamics. This construction enables fast and efficient simulation of paths of the effective dynamics, together with estimation of crucial features and observables of such dynamics, including the stationary distribution, identification of metastable states, and residence times and transition rates between them.},

keywords = {inverse problems, Machine learning, Manifold Learning, random walks, statistics, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Unsupervised learning of observation functions in state-space models by nonparametric moment methods},

author = {Qingci An and Yannis Kevrekidis and Fei Lu and Mauro Maggioni},

url = {https://arxiv.org/abs/2207.05242

https://doi.org/10.3934/fods.2023002},

doi = {10.3934/fods.2023002},

year  = {2023},

date = {2023-02-01},

urldate = {2023-02-01},

journal = {Foundations of Data Science},

keywords = {computational mathematics, hidden Markov models, inverse problems, Machine learning, optimal transport, regression, statistics, stochastic systems, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{PathBasedSpectralClustering,

title = {Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms},

author = {Anna V Little and Mauro Maggioni and James M Murphy},

url = {http://jmlr.csail.mit.edu/papers/volume21/18-085/18-085.pdf},

year  = {2019},

date = {2019-01-01},

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

volume = {21},

pages = {1-66},

keywords = {Clustering, diffusion geometry, Machine learning, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{2639-8001_2019_3_271,

title = {Learning by active nonlinear diffusion},

author = {Mauro Maggioni and James M Murphy},

url = {http://aimsciences.org//article/id/6f8fefb2-e464-48ea-b2de-f37686725966},

doi = {10.3934/fods.2019012},

issn = {A0000-0002},

year  = {2019},

date = {2019-01-01},

journal = {Foundations of Data Science},

volume = {1},

number = {"2639-8001-2019-3-271"},

pages = {271},

keywords = {Active Learning, Clustering, diffusion geometry, Machine learning, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{MAGC,

title = {Discovering and deciphering relationships across disparate data modalities},

author = {Joshua T Vogelstein and Eric W Bridgeford and Qing Wang and Carey E Priebe and Mauro Maggioni and Cencheng Shen},

url = {https://elifesciences.org/articles/41690},

doi = {10.7554/eLife.41690},

year  = {2019},

date = {2019-01-01},

journal = {eLife},

pages = {8:e41690},

keywords = {Machine learning, statistics, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{ShenEtAl2016,

title = {Dependence Discovery from Multimodal Data via Multiscale Generalized Correlation},

author = {Cencheng Shen and Carey E Priebe and Mauro Maggioni and Joshua T Vogelstein},

year  = {2016},

date = {2016-01-01},

journal = {Submitted},

keywords = {Machine learning, statistics, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{MMIEEEPath,

title = {Algorithms from Signal and Data Processing Applied to Hyperspectral Analysis: Application to Discriminating Normal and Malignant Microarray Colon Tissue Sections},

author = {Mauro Maggioni and F J Warner and Gus L Davis and Ronald R Coifman and Frank B Geshwind and Andreas C Coppi and R A DeVerse},

year  = {2004},

date = {2004-01-01},

urldate = {2004-01-01},

journal = {submitted},

keywords = {Clustering, diffusion geometry, hyperspectral imaging, imaging, Machine learning, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}