Publications

@article{LiaoMaggioni:GMRA,

title = {Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data},

author = {Wenjing Liao and Mauro Maggioni},

url = {http://jmlr.org/papers/v20/17-252.html},

year  = {2019},

date = {2019-01-01},

journal = {Journal of machine learning Research},

volume = {20},

number = {98},

pages = {1-63},

keywords = {geometric wavelets, Machine learning, multiscale analysis, statistics},

pubstate = {published},

tppubtype = {article}

}

@article{GM:mop,

title = {Multiscale Strategies for Discrete Optimal Transport},

author = {Sam Gerber and Mauro Maggioni},

url = {https://jmlr.csail.mit.edu/papers/volume18/16-108/16-108.pdf},

year  = {2017},

date = {2017-01-01},

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

number = {72},

pages = {1--32},

keywords = {multiscale analysis, optimal transport, optimization},

pubstate = {published},

tppubtype = {article}

}

@article{LMR:MGM1,

title = {Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature},

author = {Anna V Little and Mauro Maggioni and Lorenzo Rosasco},

year  = {2017},

date = {2017-01-01},

journal = {Applied and Computational Harmonic Analysis},

volume = {43},

number = {3},

pages = {504 - 567},

note = {Submitted: 2012, MIT-CSAIL-TR-2012-029/CBCL-310},

keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics},

pubstate = {published},

tppubtype = {article}

}

@article{IM:GMRA_CS,

title = {Approximation of points on low-dimensional manifolds via random linear projections},

author = {Mark A Iwen and Mauro Maggioni},

year  = {2013},

date = {2013-01-01},

journal = {Inference and Information},

volume = {2},

number = {1},

pages = {1--31},

note = {arXiv:1204.3337v1, 2012},

keywords = {Machine learning, Manifold Learning, multiscale analysis},

pubstate = {published},

tppubtype = {article}

}

@article{BM:MMDPs,

title = {Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning},

author = {Jake Bouvrie and Mauro Maggioni},

url = {http://arxiv.org/abs/1212.1143},

year  = {2012},

date = {2012-01-01},

keywords = {Machine learning, multiscale analysis, reinforcement learning, representation learning, spectral graph theory, transfer learning},

pubstate = {published},

tppubtype = {article}

}

@article{CM:MGM2,

title = {Multi-scale geometric methods for data sets II: Geometric Multi-Resolution Analysis},

author = {William K Allard and Guangliang Chen and Mauro Maggioni},

year  = {2012},

date = {2012-01-01},

journal = {Applied and Computational Harmonic Analysis},

volume = {32},

number = {3},

pages = {435--462},

keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics},

pubstate = {published},

tppubtype = {article}

}

@article{FFT2011,

title = {Multi-Resolution Geometric Analysis for data in high dimensions},

author = {Guangliang Chen and Anna V Little and Mauro Maggioni},

year  = {2011},

date = {2011-08-01},

journal = {Proc. FFT 2011},

keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis},

pubstate = {published},

tppubtype = {article}

}

@article{MM:Spars11talk,

title = {Multiscale Geometric Dictionaries for Point-Cloud Data, Presented at SPARS 11, http://www.math.duke.edu/~mauro/research.html#Talks},

author = {Mauro Maggioni},

url = {http://www.math.duke.edu/~mauro/research.html#Talks},

year  = {2011},

date = {2011-06-01},

note = {Presented at SPARS 11},

keywords = {dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis},

pubstate = {published},

tppubtype = {article}

}

@article{jms:UniformizationEigenfunctions2,

title = {Universal local manifold parametrizations via heat kernels and eigenfunctions of the Laplacian},

author = {Peter W Jones and Mauro Maggioni and Raanan Schul},

year  = {2010},

date = {2010-01-01},

journal = {Ann. Acad. Scient. Fen.},

volume = {35},

pages = {1--44},

note = {http://arxiv.org/abs/0709.1975},

keywords = {diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory},

pubstate = {published},

tppubtype = {article}

}

@article{CM:MGM3,

title = {Multiscale Geometric Methods for Data Sets III: multiple planes},

author = {Guangliang Chen and Mauro Maggioni},

year  = {2010},

date = {2010-01-01},

journal = {in preparation},

keywords = {Machine learning, Manifold Learning, multiscale analysis, statistics},

pubstate = {published},

tppubtype = {article}

}

@article{CM:geometricwaveletsciss,

title = {Multiscale Geometric Wavelets for the Analysis of Point Clouds},

author = {Guangliang Chen and Mauro Maggioni},

year  = {2010},

date = {2010-01-01},

journal = {Proc. CISS 2010},

keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, spectral graph theory},

pubstate = {published},

tppubtype = {article}

}

@article{MM:DiffusionPolynomialFrames,

title = {Diffusion polynomial frames on metric measure spaces},

author = {Mauro Maggioni and Hrushikesh Mhaskar},

year  = {2008},

date = {2008-05-01},

journal = {ACHA},

volume = {3},

pages = {329--353},

keywords = {approximation theory, diffusion geometry, heat kernels, Laplacian eigenfunctions, multiscale analysis},

pubstate = {published},

tppubtype = {article}

}

@article{jms:UniformizationEigenfunctions,

title = {Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels},

author = {Peter W Jones and Mauro Maggioni and Raanan Schul},

year  = {2008},

date = {2008-02-01},

journal = {Proc. Nat. Acad. Sci.},

volume = {105},

number = {6},

pages = {1803--1808},

keywords = {diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory},

pubstate = {published},

tppubtype = {article}

}

@article{CM:MsDataDiffWavelets,

title = {Multiscale Data Analysis with Diffusion Wavelets},

author = {Ronald R Coifman and Mauro Maggioni},

year  = {2007},

date = {2007-04-01},

journal = {Proc. SIAM Bioinf. Workshop, Minneapolis},

keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{CMDiffusionWavelets,

title = {Diffusion Wavelets},

author = {Ronald R Coifman and Mauro Maggioni},

year  = {2006},

date = {2006-07-01},

journal = {Appl. Comp. Harm. Anal.},

volume = {21},

number = {1},

pages = {53--94},

note = {(Tech. Rep. YALE/DCS/TR-1303, Yale Univ., Sep. 2004)},

keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{DiffusionWaveletPackets,

title = {Diffusion Wavelet Packets},

author = {James Jr. C Bremer and Ronald R Coifman and Mauro Maggioni and Arthur D Szlam},

year  = {2006},

date = {2006-07-01},

journal = {Appl. Comp. Harm. Anal.},

volume = {21},

number = {1},

pages = {95--112},

note = {(Tech. Rep. YALE/DCS/TR-1304, 2004)},

keywords = {diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{DiffusionPNAS2,

title = {Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods},

author = {Ronald R Coifman and S Lafon and A B Lee and Mauro Maggioni and B Nadler and Frederick Warner and Steven W Zucker},

year  = {2005},

date = {2005-01-01},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

volume = {102},

number = {21},

pages = {7432--7438},

keywords = {diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{MRA_HRBF2004,

title = {Multi-Scale Approximation with Hierarchical Radial Basis Functions Networks,},

author = {S Ferrari and Mauro Maggioni and N A Borghese},

year  = {2004},

date = {2004-01-01},

journal = {IEEE Trans. on Neural Networks},

volume = {15},

number = {1},

pages = {178--188},

keywords = {approximation theory, multiscale analysis, radial basis functions},

pubstate = {published},

tppubtype = {article}

}