2024
Wu, Yantao; Maggioni, Mauro
Conditional Regression for the Nonlinear Single-Variable Model Bachelor Thesis
2024.
Links | BibTeX | Tags: Machine learning, regression, statistics, supervised learning
@bachelorthesis{nokey,
title = {Conditional Regression for the Nonlinear Single-Variable Model},
author = {Yantao Wu and Mauro Maggioni},
url = {https://doi.org/10.48550/arXiv.2411.09686},
year = {2024},
date = {2024-11-14},
urldate = {2024-11-14},
journal = {arXiv},
keywords = {Machine learning, regression, statistics, supervised learning},
pubstate = {published},
tppubtype = {bachelorthesis}
}
2021
Liao, Wenjing; Maggioni, Mauro; Vigogna, Stefano
Multiscale regression on intrinsically low-dimensional sets Journal Article
In: Mathematics in Engineering, vol. 4, no. 4, 2021.
Links | BibTeX | Tags: Machine learning, Manifold Learning, statistics, supervised learning
@article{LiaoMaggioniVigogna:MultiscaleRegressionManifolds,
title = {Multiscale regression on intrinsically low-dimensional sets},
author = {Wenjing Liao and Mauro Maggioni and Stefano Vigogna},
url = {https://arxiv.org/abs/2101.05119v1
http://www.aimspress.com/aimspress-data/mine/2022/4/PDF/mine-04-04-028.pdf},
doi = {DOI:10.3934/mine.2022028},
year = {2021},
date = {2021-08-24},
urldate = {2021-08-24},
journal = {Mathematics in Engineering},
volume = {4},
number = {4},
keywords = {Machine learning, Manifold Learning, statistics, supervised learning},
pubstate = {published},
tppubtype = {article}
}
2020
Tomita, Tyler M; Browne, James; Shen, Cencheng; Chung, Jaewon; Patsolic, Jesse L; Falk, Benjamin; Priebe, Carey E; Yim, Jason; Burns, Randal; Maggioni, Mauro; Vogelstein, Joshua T
Sparse Projection Oblique Randomer Forests Journal Article
In: Journal of Machine Learning Research, vol. 21, no. 104, pp. 1-39, 2020.
Links | BibTeX | Tags: Machine learning, statistics, supervised learning
@article{SparseObliqueRandomerForestsb,
title = {Sparse Projection Oblique Randomer Forests},
author = {Tyler M Tomita and James Browne and Cencheng Shen and Jaewon Chung and Jesse L Patsolic and Benjamin Falk and Carey E Priebe and Jason Yim and Randal Burns and Mauro Maggioni and Joshua T Vogelstein},
url = {http://jmlr.org/papers/v21/18-664.html},
year = {2020},
date = {2020-01-01},
journal = {Journal of Machine Learning Research},
volume = {21},
number = {104},
pages = {1-39},
keywords = {Machine learning, statistics, supervised learning},
pubstate = {published},
tppubtype = {article}
}
2019
Browne, James Tomita Tyler M.; Shen, Cencheng; Chung, Jaewon; Patsolic, Jesse L; Falk, Benjamin; Priebe, Carey E Yim Jason; RandalMaggioni, Mauro Burns; Vogelstein, Joshua T
Sparse Projection Oblique Randomer Forests Journal Article
In: Journ. Mach. Learn. Res., 2019.
Links | BibTeX | Tags: Machine learning, statistics, supervised learning
@article{SparseObliqueRandomerForests,
title = {Sparse Projection Oblique Randomer Forests},
author = {James Tomita Tyler M. Browne and Cencheng Shen and Jaewon Chung and Jesse L Patsolic and Benjamin Falk and Carey E Yim Jason Priebe and Mauro Burns RandalMaggioni and Joshua T Vogelstein},
url = {https://arxiv.org/pdf/1506.03410.pdf},
year = {2019},
date = {2019-01-01},
journal = {Journ. Mach. Learn. Res.},
keywords = {Machine learning, statistics, supervised learning},
pubstate = {published},
tppubtype = {article}
}
2017
Tomita, Tyler M; Maggioni, Mauro; Vogelstein, Joshua T
ROFLMAO: Robust Oblique Forests with Linear MAtrix Operations Proceedings Article
In: SIAM Data Mining, 2017.
BibTeX | Tags: Machine learning, statistics, supervised learning
@inproceedings{RerF,
title = {ROFLMAO: Robust Oblique Forests with Linear MAtrix Operations},
author = {Tyler M Tomita and Mauro Maggioni and Joshua T Vogelstein},
year = {2017},
date = {2017-01-01},
booktitle = {SIAM Data Mining},
keywords = {Machine learning, statistics, supervised learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Tomita, Tyler; Maggioni, Mauro; Vogelstein, Joshua T
ROFLMAO: Robust Oblique Forests with Linear MAtrix Operations Proceedings Article
In: 2017.
BibTeX | Tags: Machine learning, statistics, supervised learning
@inproceedings{TMJ:ROFLMAO,
title = {ROFLMAO: Robust Oblique Forests with Linear MAtrix Operations},
author = {Tyler Tomita and Mauro Maggioni and Joshua T Vogelstein},
year = {2017},
date = {2017-01-01},
keywords = {Machine learning, statistics, supervised learning},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Liao, Wenjing; Maggioni, Mauro; Vigogna, S
Learning adaptive multiscale approximations to data and functions near low-dimensional sets Proceedings Article
In: Proceedings of the IEEE Information Theory Workshop, 2016, (Cambridge, UK).
BibTeX | Tags: Machine learning, Manifold Learning, statistics, supervised learning
@inproceedings{LMV:IEEE2016InformationTheory,
title = {Learning adaptive multiscale approximations to data and functions near low-dimensional sets},
author = {Wenjing Liao and Mauro Maggioni and S Vigogna},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the IEEE Information Theory Workshop},
note = {Cambridge, UK},
keywords = {Machine learning, Manifold Learning, statistics, supervised learning},
pubstate = {published},
tppubtype = {inproceedings}
}
2015
Tomita, Tyler M; Maggioni, Mauro; Vogelstein, Joshua T
Randomer Forests Journal Article
In: arXiv preprint arXiv:1506.03410, 2015.
Links | BibTeX | Tags: Machine learning, statistics, supervised learning
@article{Tomita2017b,
title = {Randomer Forests},
author = {Tyler M Tomita and Mauro Maggioni and Joshua T Vogelstein},
url = {https://arxiv.org/abs/1506.03410},
year = {2015},
date = {2015-01-01},
journal = {arXiv preprint arXiv:1506.03410},
keywords = {Machine learning, statistics, supervised learning},
pubstate = {published},
tppubtype = {article}
}
2006
Maggioni, Mauro; Davis, Gus L; Warner, F J; Geshwind, Frank B; Coppi, Andreas C; DeVerse, R A; Coifman, Ronald R
Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections Conference
vol. 6091, no. 1, SPIE, San Jose, CA, USA, 2006.
BibTeX | Tags: Clustering, hyperspectral imaging, imaging, Machine learning, supervised learning
@conference{maggioni:60910I,
title = {Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections},
author = {Mauro Maggioni and Gus L Davis and F J Warner and Frank B Geshwind and Andreas C Coppi and R A DeVerse and Ronald R Coifman},
editor = {Robert R Alfano and Alvin Katz},
year = {2006},
date = {2006-01-01},
journal = {Optical Biopsy VI},
volume = {6091},
number = {1},
pages = {60910I},
publisher = {SPIE},
address = {San Jose, CA, USA},
keywords = {Clustering, hyperspectral imaging, imaging, Machine learning, supervised learning},
pubstate = {published},
tppubtype = {conference}
}
- Lectures at Summer School at Peking University, July 2017.
- PCMI Lectures, Summer 2016: Lecture 1, Lecture 2, Problems/discussion points
- Google Scholar
- Papers on the ArXiv
- Papers on MathsciNet
- Tutorials on diffusion geometry and multiscale analysis on graphs at the MRA Internet Program at IPAM: Part I and Part II.
- Diffusion Geometries, Diffusion Wavelets and Harmonic Analysis of large data sets, IPAM, Multiscale Geometric Analysis Program, Fall 2004.
- Diffusion Geometries, global and multiscale, IPAM, 2005.