2021
Mauro Maggioni Fei Lu, Sui Tang
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories Journal Article
In: Journ. Mach. Learn. res., vol. 2, no. 32, pp. 1–67, 2021.
Abstract | Links | BibTeX | Tags: Active Learning, interacting particle systems, inverse problems, Machine learning
@article{LuMMTang21,
title = {Learning interaction kernels in heterogeneous systems of agents from multiple trajectories},
author = {Fei Lu, Mauro Maggioni, Sui Tang},
url = {https://jmlr.csail.mit.edu/papers/v22/19-861.html},
year = {2021},
date = {2021-01-01},
journal = {Journ. Mach. Learn. res.},
volume = {2},
number = {32},
pages = {1--67},
abstract = {Systems of interacting particles, or agents, have wide applications in many disciplines, including Physics, Chemistry, Biology and Economics. These systems are governed by interaction laws, which are often unknown: estimating them from observation data is a fundamental task that can provide meaningful insights and accurate predictions of the behaviour of the agents. In this paper, we consider the inverse problem of learning interaction laws given data from multiple trajectories, in a nonparametric fashion, when the interaction kernels depend on pairwise distances. We establish a condition for learnability of interaction kernels, and construct an estimator based on the minimization of a suitably regularized least squares functional, that is guaranteed to converge, in a suitable L^2 space, at the optimal min-max rate for 1-dimensional nonparametric regression. We propose an efficient learning algorithm to construct such estimator, which can be implemented in parallel for multiple trajectories and is therefore well-suited for the high dimensional, big data regime. Numerical simulations on a variety examples, including opinion dynamics, predator-prey and swarm dynamics and heterogeneous particle dynamics, suggest that the learnability condition is satisfied in models used in practice, and the rate of convergence of our estimator is consistent with the theory. These simulations also suggest that our estimators are robust to noise in the observations, and can produce accurate predictions of trajectories in large time intervals, even when they are learned from observations in short time intervals.},
keywords = {Active Learning, interacting particle systems, inverse problems, Machine learning},
pubstate = {published},
tppubtype = {article}
}
2019
Murphy, James M; Maggioni, Mauro
Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion Journal Article
In: IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 3, pp. 1829-1845, 2019, ISSN: 1558-0644.
Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging
@article{8481477,
title = {Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion},
author = {James M Murphy and Mauro Maggioni},
doi = {10.1109/TGRS.2018.2869723},
issn = {1558-0644},
year = {2019},
date = {2019-03-01},
urldate = {2019-03-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {57},
number = {3},
pages = {1829-1845},
keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging},
pubstate = {published},
tppubtype = {article}
}
Maggioni, Mauro; Murphy, James M
Learning by active nonlinear diffusion Journal Article
In: Foundations of Data Science, vol. 1, no. “2639-8001-2019-3-271”, pp. 271, 2019, ISSN: A0000-0002.
Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, Machine learning, Unsupervised Learning
@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}
}
2018
Murphy, James M; Maggioni, Mauro
Iterative Active Learning with Diffusion Geometry for Hyperspectral Images Proceedings Article
In: Proc. of WHISPERS, 2018.
Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging, Machine learning
@inproceedings{whispers2018,
title = {Iterative Active Learning with Diffusion Geometry for Hyperspectral Images},
author = {James M Murphy and Mauro Maggioni},
url = {https://ieeexplore.ieee.org/abstract/document/8747033},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Proc. of WHISPERS},
keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging, Machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Murphy, James M; Maggioni, Mauro
Diffusion geometric methods for fusion of remotely sensed data Proceedings Article
In: Velez-Reyes, Miguel; Messinger, David W (Ed.): Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, pp. 137 – 147, International Society for Optics and Photonics SPIE, 2018.
Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging, Machine learning, Unsupervised Learning
@inproceedings{10.1117/12.2305274,
title = {Diffusion geometric methods for fusion of remotely sensed data},
author = {James M Murphy and Mauro Maggioni},
editor = {Miguel Velez-Reyes and David W Messinger},
url = {https://doi.org/10.1117/12.2305274},
doi = {10.1117/12.2305274},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV},
volume = {10644},
pages = {137 -- 147},
publisher = {SPIE},
organization = {International Society for Optics and Photonics},
keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging, Machine learning, Unsupervised Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Wang, Yang; Chen, Guangliang; Maggioni, Mauro
High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies Journal Article
In: IEEE Journal of selected topics in applied Earth observations and remote sensing, vol. 9, no. 9, pp. 4316–4324, 2016.
Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging
@article{WCM:HSIandMovies,
title = {High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies},
author = {Yang Wang and Guangliang Chen and Mauro Maggioni},
url = {https://arxiv.org/abs/1509.07497
},
doi = {10.1109/JSTARS.2016.2539968},
year = {2016},
date = {2016-05-16},
urldate = {2016-05-16},
journal = {IEEE Journal of selected topics in applied Earth observations and remote sensing},
volume = {9},
number = {9},
pages = {4316--4324},
keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging},
pubstate = {published},
tppubtype = {article}
}
2015
Maggioni, Mauro Y. Wang; Chen, Guangliang
Enhanced Detection of Chemical Plumes in Hyperspectral Images and Movies through Improved Background Modeling Proceedings Article
In: Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015.
Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging
@inproceedings{WangChenMaggioni:Whispers15,
title = {Enhanced Detection of Chemical Plumes in Hyperspectral Images and Movies through Improved Background Modeling},
author = {Mauro Y. Wang Maggioni and Guangliang Chen},
url = {https://www.sjsu.edu/faculty/guangliang.chen/papers/ChenMaggioniWang_workshop.pdf},
doi = {10.1109/WHISPERS.2015.8075369},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral imaging, imaging},
pubstate = {published},
tppubtype = {inproceedings}
}
- 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.