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}
}
Loeffler, Shane E.; Ahmad, Zan; Ali, Syed Yusuf; Yamamoto, Carolyna; Popescu, Dan M.; Yee, Alana; Lal, Yash; Trayanova, Natalia; Maggioni, Mauro
2024.
Abstract | Links | BibTeX | Tags: digital twins, Laplacian eigenfunctions, neural networks, PDEs, precision medicine
@conference{nokey,
title = {Graph Fourier Neural Kernels (G-FuNK): Learning Solutions of Nonlinear Diffusive Parametric PDEs on Multiple Domains},
author = {Shane E. Loeffler and Zan Ahmad and Syed Yusuf Ali and Carolyna Yamamoto and Dan M. Popescu and Alana Yee and Yash Lal and Natalia Trayanova and Mauro Maggioni},
url = {https://doi.org/10.48550/arXiv.2410.04655},
year = {2024},
date = {2024-10-09},
urldate = {2024-10-09},
abstract = {Predicting time-dependent dynamics of complex systems governed by non-linear partial differential equations (PDEs) with varying parameters and domains is a challenging task motivated by applications across various fields. We introduce a novel family of neural operators based on our Graph Fourier Neural Kernels, designed to learn solution generators for nonlinear PDEs in which the highest-order term is diffusive, across multiple domains and parameters. G-FuNK combines components that are parameter- and domain-adapted with others that are not. The domain-adapted components are constructed using a weighted graph on the discretized domain, where the graph Laplacian approximates the highest-order diffusive term, ensuring boundary condition compliance and capturing the parameter and domain-specific behavior. Meanwhile, the learned components transfer across domains and parameters using our variant Fourier Neural Operators. This approach naturally embeds geometric and directional information, improving generalization to new test domains without need for retraining the network. To handle temporal dynamics, our method incorporates an integrated ODE solver to predict the evolution of the system. Experiments show G-FuNK's capability to accurately approximate heat, reaction diffusion, and cardiac electrophysiology equations across various geometries and anisotropic diffusivity fields. G-FuNK achieves low relative errors on unseen domains and fiber fields, significantly accelerating predictions compared to traditional finite-element solvers.},
keywords = {digital twins, Laplacian eigenfunctions, neural networks, PDEs, precision medicine},
pubstate = {published},
tppubtype = {conference}
}
Kuemmerle, Christian; Maggioni, Mauro; Tang, Sui
Learning Transition Operators From Sparse Space-Time Samples Journal Article
In: IEEE Transactions on Information Theory, 2024.
Abstract | Links | BibTeX | Tags: dynamical systems, Machine learning, optimization, sparsity, statistics
@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}
}
Bayrakta, E; Lu, F; Maggioni, M; Wu, R; Yang, S
Probabilistic cellular automata with local transition matrices: synchronization, ergodicity, and inference Journal Article
In: arXiv preprint arXiv:2405.02928, 2024.
Links | BibTeX | Tags: inverse problems, Machine learning, statistics
@article{PCAlocaltransitionmatrices,
title = {Probabilistic cellular automata with local transition matrices: synchronization, ergodicity, and inference},
author = {E Bayrakta and F Lu and M Maggioni and R Wu and S Yang
},
url = {https://arxiv.org/html/2405.02928v2},
year = {2024},
date = {2024-05-03},
urldate = {2024-05-03},
journal = {arXiv preprint arXiv:2405.02928},
keywords = {inverse problems, Machine learning, statistics},
pubstate = {published},
tppubtype = {article}
}
Yin, Minglang; Charon, Nicolas; Brody, Ryan; Lu, Lu; Trayanova, Natalia; Maggioni, Mauro
A scalable framework for learning the geometry-dependent solution operators of partial differential equations Journal Article
In: Nature Computational Science, 2024.
Abstract | Links | BibTeX | Tags: digital twins, Machine learning, model reduction, neural networks, PDEs, precision medicine
@article{DIMON2024,
title = {A scalable framework for learning the geometry-dependent solution operators of partial differential equations},
author = {Minglang Yin and Nicolas Charon and Ryan Brody and Lu Lu and Natalia Trayanova and Mauro Maggioni},
url = {https://arxiv.org/pdf/2402.07250.pdf
https://www.nature.com/articles/s43588-024-00732-2
https://github.com/MinglangYin/DIMON},
doi = {10.1038/s43588-024-00732-2},
year = {2024},
date = {2024-02-13},
urldate = {2024-02-13},
journal = {Nature Computational Science},
abstract = {The solution of a PDE over varying initial/boundary conditions on multiple domains is needed in a wide variety of applications, but it is computationally expensive if the solution is computed de novo whenever the initial/boundary conditions of the domain change. We introduce a general operator learning framework, called DIffeomorphic Mapping Operator learNing (DIMON) to learn approximate PDE solutions over a family of domains ${Omega_{theta}}_theta$, that learns the map from initial/boundary conditions and domain $Omega_theta$ to the solution of the PDE, or to specified functionals thereof. DIMON is based on transporting a given problem (initial/boundary conditions and domain $Omega_theta$) to a problem on a reference domain $Omega_0$, where training data from multiple problems is used to learn the map to the solution on $Omega_0$, which is then re-mapped to the original domain $Omega_theta$. We consider several problems to demonstrate the performance of the framework in learning both static and time-dependent PDEs on non-rigid geometries; these include solving the Laplace equation, reaction-diffusion equations, and a multiscale PDE that characterizes the electrical propagation on the left ventricle. This work paves the way toward the fast prediction of PDE solutions on a family of domains and the application of neural operators in engineering and precision medicine.},
keywords = {digital twins, Machine learning, model reduction, neural networks, PDEs, precision medicine},
pubstate = {published},
tppubtype = {article}
}
Lang, Quanjun; Wang, Xiong; Lu, Fei; Maggioni, Mauro
Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel Bachelor Thesis
2024.
Links | BibTeX | Tags: interacting particle systems, Machine learning, optimization, statistics, stochastic systems
@bachelorthesis{nokey,
title = {Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel},
author = {Quanjun Lang and Xiong Wang and Fei Lu and Mauro Maggioni},
url = {https://arxiv.org/pdf/2402.08412.pdf},
year = {2024},
date = {2024-02-13},
journal = {arXiv},
keywords = {interacting particle systems, Machine learning, optimization, statistics, stochastic systems},
pubstate = {published},
tppubtype = {bachelorthesis}
}
2023
Ye, Felix X. -F.; Yang, Sichen; Maggioni, Mauro
Nonlinear model reduction for slow-fast stochastic systems near manifolds Journal Article
In: J Nonlinear Sci, vol. 34, iss. 1, no. 22, 2023.
Abstract | Links | BibTeX | Tags: inverse problems, Machine learning, Manifold Learning, random walks, statistics, Unsupervised Learning
@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}
}
Miller, Jason; Tang, Sui; Zhong, Ming; Maggioni, Mauro
Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems Journal Article
In: Sampling Theory, Signal Processing, and Data Analysis , vol. 21, 2023.
Links | BibTeX | Tags: agent-based models, interacting particle systems, inverse problems, Machine learning
@article{LearningInteractionkernels2ndorder,
title = {Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems},
author = {Jason Miller and Sui Tang and Ming Zhong and Mauro Maggioni},
url = {https://arxiv.org/abs/2010.03729},
doi = {https://doi.org/10.1007/s43670-023-00055-9},
year = {2023},
date = {2023-04-12},
urldate = {2023-04-12},
journal = { Sampling Theory, Signal Processing, and Data Analysis },
volume = {21},
keywords = {agent-based models, interacting particle systems, inverse problems, Machine learning},
pubstate = {published},
tppubtype = {article}
}
An, Qingci; Kevrekidis, Yannis; Lu, Fei; Maggioni, Mauro
Unsupervised learning of observation functions in state-space models by nonparametric moment methods Journal Article
In: Foundations of Data Science, 2023.
Links | BibTeX | Tags: computational mathematics, hidden Markov models, inverse problems, Machine learning, optimal transport, regression, statistics, stochastic systems, Unsupervised Learning
@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}
}
2022
Feng, Jinchao; Maggioni, Mauro; Martin, Patrick; Zhong, Ming
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems Proceedings Article
In: IFAC Proceedings, 2022.
Abstract | Links | BibTeX | Tags: agent-based models, inverse problems, Machine learning, statistics
@inproceedings{nokey,
title = {Learning Interaction Variables and Kernels from Observations of Agent-Based Systems},
author = {Jinchao Feng and Mauro Maggioni and Patrick Martin and Ming Zhong},
url = {https://arxiv.org/abs/2208.02758
},
doi = {https://doi.org/10.1016/j.ifacol.2022.11.046},
year = {2022},
date = {2022-08-04},
urldate = {2022-08-04},
booktitle = {IFAC Proceedings},
journal = {arXiV preprint},
abstract = {Dynamical systems across many disciplines are modeled as interacting particles or agents, with interaction rules that depend on a very small number of variables (e.g. pairwise distances, pairwise differences of phases, etc...), functions of the state of pairs of agents. Yet, these interaction rules can generate self-organized dynamics, with complex emergent behaviors (clustering, flocking, swarming, etc.). We propose a learning technique that, given observations of states and velocities along trajectories of the agents, yields both the variables upon which the interaction kernel depends and the interaction kernel itself, in a nonparametric fashion. This yields an effective dimension reduction which avoids the curse of dimensionality from the high-dimensional observation data (states and velocities of all the agents). We demonstrate the learning capability of our method to a variety of first-order interacting systems.},
keywords = {agent-based models, inverse problems, Machine learning, statistics},
pubstate = {published},
tppubtype = {inproceedings}
}
Popescu, Dan M.; Shade, Julie K.; Lai, Changxin; Aronis, Konstantinos N.; David Ouyang,; Moorthy, M. Vinayaga; Cook, Nancy R.; Lee, Daniel C.; Kadish, Alan; Albert, Christine M.; Wu, Katherine C.; Maggioni, Mauro; Trayanova, Natalia A.
Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart Journal Article
In: Nature Cardiovascular Research, 2022.
Links | BibTeX | Tags: digital twins, Machine learning, medical imaging, neural networks, precision medicine
@article{SCDsurvival1,
title = {Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart},
author = {Dan M. Popescu and Julie K. Shade and Changxin Lai and Konstantinos N. Aronis and David Ouyang, and M. Vinayaga Moorthy and Nancy R. Cook and Daniel C. Lee and Alan Kadish and Christine M. Albert and Katherine C. Wu and Mauro Maggioni and Natalia A. Trayanova
},
url = {https://rdcu.be/cKSAl},
doi = {10.1038/s44161-022-00041-9},
year = {2022},
date = {2022-03-07},
urldate = {2022-03-07},
journal = {Nature Cardiovascular Research},
keywords = {digital twins, Machine learning, medical imaging, neural networks, precision medicine},
pubstate = {published},
tppubtype = {article}
}
Lanteri, Alessandro; Maggioni, Mauro; Vigogna, Stefano
Conditional regression for single-index models Journal Article
In: Bernoulli, 2022.
Links | BibTeX | Tags: regression, statistics
@article{aless2020conditional,
title = {Conditional regression for single-index models},
author = {Alessandro Lanteri and Mauro Maggioni and Stefano Vigogna},
url = {https://www.e-publications.org/ims/submission/BEJ/user/submissionFile/49273?confirm=22a655d7
https://arxiv.org/abs/2002.10008},
doi = {10.3150/22-BEJ1482},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Bernoulli},
keywords = {regression, statistics},
pubstate = {published},
tppubtype = {article}
}
2021
Abramson, Haley G.; Popescu, Dan M.; Yu, Rebecca; Lai, Changxin; Shade, Julie K.; Wu, Katherine C.; Maggioni, Mauro; Trayanova, Natalia A.
Anatomically-Informed Deep Learning on Contrast-Enhanced Cardiac MRI for Scar Segmentation and Clinical Feature Extraction Journal Article
In: Cardiovascular Digital Health Journal, 2021.
Links | BibTeX | Tags: digital twins, imaging, Machine learning, medical imaging
@article{AnatLGECMRInn,
title = {Anatomically-Informed Deep Learning on Contrast-Enhanced Cardiac MRI for Scar Segmentation and Clinical Feature Extraction},
author = {Haley G. Abramson and Dan M. Popescu and Rebecca Yu and Changxin Lai and Julie K. Shade and Katherine C. Wu and Mauro Maggioni and Natalia A. Trayanova},
url = {https://arxiv.org/abs/2010.11081
https://www.cvdigitalhealthjournal.com/article/S2666-6936(21)00131-6/pdf
https://www.ahajournals.org/doi/abs/10.1161/circ.142.suppl_3.16017
http://jhu.technologypublisher.com/technology/43121},
doi = {https://doi.org/10.1016/j.cvdhj.2021.11.007},
year = {2021},
date = {2021-11-25},
urldate = {2021-11-25},
journal = {Cardiovascular Digital Health Journal},
keywords = {digital twins, imaging, Machine learning, medical imaging},
pubstate = {published},
tppubtype = {article}
}
Zhong, Ming; Miller, Jason; Maggioni, Mauro
Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides Unpublished
2021.
Links | BibTeX | Tags: agent-based models, interacting particle systems, Machine learning
@unpublished{zhongEphermerids,
title = {Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides},
author = {Ming Zhong and Jason Miller and Mauro Maggioni},
url = {https://arxiv.org/abs/2108.11894},
year = {2021},
date = {2021-08-26},
urldate = {2021-08-26},
journal = {arXiv preprint},
keywords = {agent-based models, interacting particle systems, Machine learning},
pubstate = {published},
tppubtype = {unpublished}
}
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}
}
Lu, Fei; Maggioni, Mauro; Tang, Sui
Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories Journal Article
In: Foundation of Computational Mathematics, 2021.
Abstract | Links | BibTeX | Tags: agent-based models, interacting particle systems, Machine learning, statistics, stochastic systems
@article{learningStochasticInteracting,
title = {Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories},
author = {Fei Lu and Mauro Maggioni and Sui Tang},
url = {https://arxiv.org/abs/2007.15174
https://link.springer.com/content/pdf/10.1007/s10208-021-09521-z.pdf},
doi = {doi.org/10.1007/s10208-021-09521-z},
year = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
journal = {Foundation of Computational Mathematics},
abstract = {We consider stochastic systems of interacting particles or agents, with dynamics determined by an interaction kernel which only depends on pairwise distances. We study the problem of inferring this interaction kernel from observations of the positions of the particles, in either continuous or discrete time, along multiple independent trajectories. We introduce a nonparametric inference approach to this inverse problem, based on a regularized maximum likelihood estimator constrained to suitable hypothesis spaces adaptive to data. We show that a coercivity condition enables us to control the condition number of this problem and prove the consistency of our estimator, and that in fact it converges at a near-optimal learning rate, equal to the min-max rate of 1-dimensional non-parametric regression. In particular, this rate is independent of the dimension of the state space, which is typically very high. We also analyze the discretization errors in the case of discrete-time observations, showing that it is of order 1/2 in terms of the time spacings between observations. This term, when large, dominates the sampling error and the approximation error, preventing convergence of the estimator. Finally, we exhibit an efficient parallel al- gorithm to construct the estimator from data, and we demonstrate the effectiveness of our algorithm with numerical tests on prototype systems including stochastic opinion dynamics and a Lennard-Jones model.},
keywords = {agent-based models, interacting particle systems, Machine learning, statistics, stochastic systems},
pubstate = {published},
tppubtype = {article}
}
Lu, Fei; Li, Zhongyang; Maggioni, Mauro; Tang, Sui; Zhang, Cheng
On the identifiability of interaction functions in systems of interacting particles Journal Article
In: Stochastic Processes and their Applications, vol. 132, 2021.
Links | BibTeX | Tags: agent-based models, interacting particle systems, inverse problems, Machine learning, model reduction, statistics
@article{IdentifiabilityInteractionFunctions,
title = {On the identifiability of interaction functions in systems of interacting particles},
author = {Fei Lu and Zhongyang Li and Mauro Maggioni and Sui Tang and Cheng Zhang},
url = {https://arxiv.org/abs/1912.11965},
doi = {https://doi.org/10.1016/j.spa.2020.10.005},
year = {2021},
date = {2021-02-01},
journal = {Stochastic Processes and their Applications},
volume = {132},
keywords = {agent-based models, interacting particle systems, inverse problems, Machine learning, model reduction, statistics},
pubstate = {published},
tppubtype = {article}
}
Jason Miller Mauro Maggioni, Hongda Qiu
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds Proceedings
ICML, 2021.
Abstract | Links | BibTeX | Tags: agent-based models, interacting particle systems, Machine learning, model reduction, statistics
@proceedings{AgentSystemsManifolds,
title = { Learning Interaction Kernels for Agent Systems on Riemannian Manifolds},
author = {Mauro Maggioni, Jason Miller, Hongda Qiu, Ming Zhong},
url = {http://proceedings.mlr.press/v139/maggioni21a.html
https://icml.cc/virtual/2021/poster/10167
https://arxiv.org/abs/2102.00327},
year = {2021},
date = {2021-01-30},
urldate = {2021-01-30},
abstract = {Interacting agent and particle systems are extensively used to model complex phenomena in science and engineering. We consider the problem of learning interaction kernels in these dynamical systems constrained to evolve on Riemannian manifolds from given trajectory data. The models we consider are based on interaction kernels depending on pairwise Riemannian distances between agents, with agents interacting locally along the direction of the shortest geodesic connecting them. We show that our estimators converge at a rate that is independent of the dimension of the state space, and derive bounds on the trajectory estimation error, on the manifold, between the observed and estimated dynamics. We demonstrate the performance of our estimator on two classical first order interacting systems: Opinion Dynamics and a Predator-Swarm system, with each system constrained on two prototypical manifolds, the 2-dimensional sphere and the Poincaré disk model of hyperbolic space.},
howpublished = {ICML},
keywords = {agent-based models, interacting particle systems, Machine learning, model reduction, statistics},
pubstate = {published},
tppubtype = {proceedings}
}
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}
}
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}
}
Okada, David Jason Miller; Jonathan Chrispin; Adityo Prakosa; Natalia Trayanova; Steven Jones; Mauro Maggioni; Katherine Wu R ; C David R.
Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients with Ischemic Cardiomyopathy Journal Article
In: Circulation: Arrhythmia and Electrophysiology, 2020.
Links | BibTeX | Tags: imaging, Laplacian eigenfunctions, medical imaging
@article{SpatialComplexity1,
title = {Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients with Ischemic Cardiomyopathy},
author = {David Jason Miller; Jonathan Chrispin; Adityo Prakosa; Natalia Trayanova; Steven Jones; Mauro Maggioni; Katherine Wu R ; C David R. Okada},
url = {https://www.ahajournals.org/doi/epub/10.1161/CIRCEP.119.007975},
year = {2020},
date = {2020-01-01},
journal = {Circulation: Arrhythmia and Electrophysiology},
keywords = {imaging, Laplacian eigenfunctions, medical imaging},
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; Miller, Jason; Zhong, Ming
Data-driven Discovery of Emergent Behaviors in Collective Dynamics Journal Article
In: Physica D: Nonlinear Phenomena, 2019.
Links | BibTeX | Tags: agent-based models, interacting particle systems, inverse problems, Machine learning, model reduction, statistics
@article{DiscoveryEmergentBehaviors,
title = {Data-driven Discovery of Emergent Behaviors in Collective Dynamics},
author = {Mauro Maggioni and Jason Miller and Ming Zhong},
url = {https://arxiv.org/abs/1912.11123},
doi = {https://doi.org/10.1016/j.physd.2020.132542},
year = {2019},
date = {2019-01-01},
journal = {Physica D: Nonlinear Phenomena},
keywords = {agent-based models, interacting particle systems, inverse problems, Machine learning, model reduction, statistics},
pubstate = {published},
tppubtype = {article}
}
Little, Anna V; Maggioni, Mauro; Murphy, James M
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms Journal Article
In: Journ. Mach. Learn. Res., vol. 21, pp. 1-66, 2019.
Links | BibTeX | Tags: Clustering, diffusion geometry, Machine learning, Unsupervised Learning
@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}
}
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}
}
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}
}
Lu, Fei; Zhong, Ming; Tang, Sui; Maggioni, Mauro
Nonparametric inference of interaction laws in systems of agents from trajectory data Journal Article
In: Proceedings of the National Academy of Sciences, vol. 116, no. 29, pp. 14424–14433, 2019, ISSN: 0027-8424.
Links | BibTeX | Tags: agent-based models, interacting particle systems, inverse problems, Machine learning, model reduction, statistics
@article{LMTZ:AgentsNonParametric,
title = {Nonparametric inference of interaction laws in systems of agents from trajectory data},
author = {Fei Lu and Ming Zhong and Sui Tang and Mauro Maggioni},
url = {https://www.pnas.org/content/116/29/14424},
doi = {10.1073/pnas.1822012116},
issn = {0027-8424},
year = {2019},
date = {2019-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {116},
number = {29},
pages = {14424--14433},
publisher = {National Academy of Sciences},
keywords = {agent-based models, interacting particle systems, inverse problems, Machine learning, model reduction, statistics},
pubstate = {published},
tppubtype = {article}
}
Vogelstein, Joshua T; Bridgeford, Eric W; Wang, Qing; Priebe, Carey E; Maggioni, Mauro; Shen, Cencheng
Discovering and deciphering relationships across disparate data modalities Journal Article
In: eLife, pp. 8:e41690, 2019.
Links | BibTeX | Tags: Machine learning, statistics, Unsupervised Learning
@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}
}
Liao, Wenjing; Maggioni, Mauro
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data Journal Article
In: Journal of machine learning Research, vol. 20, no. 98, pp. 1-63, 2019.
Links | BibTeX | Tags: geometric wavelets, Machine learning, multiscale analysis, statistics
@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}
}
2018
Escande, Paul; Maggioni, Mauro
Multiscale Approximations of Transformations Journal Article
In: in preparation, 2018.
BibTeX | Tags:
@article{Escande:Muscadet,
title = {Multiscale Approximations of Transformations},
author = {Paul Escande and Mauro Maggioni},
year = {2018},
date = {2018-01-01},
journal = {in preparation},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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}
}
2017
Maggioni, Mauro
Geometric Measure Estimation Journal Article
In: in preparation, 2017.
BibTeX | Tags: geometric wavelets, statistics
@article{CIM:geometricdensityestimation,
title = {Geometric Measure Estimation},
author = {Mauro Maggioni},
year = {2017},
date = {2017-01-01},
journal = {in preparation},
keywords = {geometric wavelets, statistics},
pubstate = {published},
tppubtype = {article}
}
Crosskey, Miles C; Maggioni, Mauro
ATLAS: A geometric approach to learning high-dimensional stochastic systems near manifolds Journal Article
In: Journal of Multiscale Modeling and Simulation, vol. 15, no. 1, pp. 110–156, 2017, (arxiv: 1404.0667).
Links | BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, statistics, stochastic systems
@article{CM:ATLAS,
title = {ATLAS: A geometric approach to learning high-dimensional stochastic systems near manifolds},
author = {Miles C Crosskey and Mauro Maggioni},
url = {https://arxiv.org/abs/1404.0667
https://doi.org/10.1137/140970951},
year = {2017},
date = {2017-01-01},
journal = {Journal of Multiscale Modeling and Simulation},
volume = {15},
number = {1},
pages = {110--156},
note = {arxiv: 1404.0667},
keywords = {diffusion geometry, Machine learning, Manifold Learning, statistics, stochastic systems},
pubstate = {published},
tppubtype = {article}
}
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}
}
Gerber, Sam; Maggioni, Mauro
Multiscale Strategies for Discrete Optimal Transport Journal Article
In: Journ. Mach. Learn. Res., no. 72, pp. 1–32, 2017.
Links | BibTeX | Tags: multiscale analysis, optimal transport, optimization
@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}
}
Little, Anna V; Maggioni, Mauro; Rosasco, Lorenzo
Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature Journal Article
In: Applied and Computational Harmonic Analysis, vol. 43, no. 3, pp. 504 – 567, 2017, (Submitted: 2012, MIT-CSAIL-TR-2012-029/CBCL-310).
BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics
@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}
}
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}
}
Shen, Cencheng; Priebe, Carey E; Maggioni, Mauro; Vogelstein, Joshua T
Dependence Discovery from Multimodal Data via Multiscale Generalized Correlation Journal Article
In: Submitted, 2016.
BibTeX | Tags: Machine learning, statistics, Unsupervised Learning
@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}
}
Yin, Rachel; Monson, Eric; Honig, Elisabeth; Daubechies, Ingrid; Maggioni, Mauro
Object recognition in art drawings: Transfer of a neural network Proceedings Article
In: Proc. IEEE ICASSP, 2016.
BibTeX | Tags: imaging, Machine learning, neural networks, transfer learning
@inproceedings{YinArtDrawings,
title = {Object recognition in art drawings: Transfer of a neural network},
author = {Rachel Yin and Eric Monson and Elisabeth Honig and Ingrid Daubechies and Mauro Maggioni},
year = {2016},
date = {2016-01-01},
booktitle = {Proc. IEEE ICASSP},
keywords = {imaging, Machine learning, neural networks, transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Bongini, Mattia; Fornasier, Massimo; Hansen, M; Maggioni, Mauro
Inferring Interaction Rules From Observations of Evolutive Systems I: The Variational Approach journal
2016.
Links | BibTeX | Tags: agent-based models, interacting particle systems, Machine learning, statistics
@journal{BFHM:LearningInteractionRulesI,
title = {Inferring Interaction Rules From Observations of Evolutive Systems I: The Variational Approach},
author = {Mattia Bongini and Massimo Fornasier and M Hansen and Mauro Maggioni},
url = {https://arxiv.org/pdf/1602.00342.pdf},
doi = {https://doi.org/10.1142/S0218202517500208},
year = {2016},
date = {2016-01-01},
journal = {Mathematical Models and Methods in Applied Sciences},
volume = {27},
number = {05},
pages = {909-951},
keywords = {agent-based models, interacting particle systems, Machine learning, statistics},
pubstate = {published},
tppubtype = {journal}
}
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}
}
Maggioni, Mauro; Minsker, Stanislav; Strawn, Nate
Multiscale Dictionary Learning: Non-asymptotic Bounds and Robustness Journal Article
In: J. Mach. Learn. Res., vol. 17, no. 1, pp. 43–93, 2016, ISSN: 1532-4435.
Links | BibTeX | Tags: dictionary learning, Manifold Learning, multi-resolution analysis, robustness, sparsity
@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}
}
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}
}
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}
}
2014
Altemose, Nicolas; Miga, Karen H; Maggioni, Mauro; Willard, Huntington F
Genomic Characterization of Large Heterochromatic Gaps in the Human Genome Assembly Journal Article
In: PLoS Comput Biol, vol. 10, no. 5, pp. e1003628, 2014.
Links | BibTeX | Tags: spectral clustering
@article{10.1371/journal.pcbi.1003628,
title = {Genomic Characterization of Large Heterochromatic Gaps in the Human Genome Assembly},
author = {Nicolas Altemose and Karen H Miga and Mauro Maggioni and Huntington F Willard},
url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1003628},
doi = {10.1371/journal.pcbi.1003628},
year = {2014},
date = {2014-01-01},
journal = {PLoS Comput Biol},
volume = {10},
number = {5},
pages = {e1003628},
publisher = {Public Library of Science},
keywords = {spectral clustering},
pubstate = {published},
tppubtype = {article}
}
2013
Maggioni, Mauro
Geometric Estimation of Probability Measures in High Dimensions Proceedings Article
In: IEEE Asilomar Conference on Signals, Systems and Computers, 2013.
BibTeX | Tags: geometric wavelets, statistics
@inproceedings{MM_GeometricEstimationAsilomar,
title = {Geometric Estimation of Probability Measures in High Dimensions},
author = {Mauro Maggioni},
year = {2013},
date = {2013-01-01},
booktitle = {IEEE Asilomar Conference on Signals, Systems and Computers},
keywords = {geometric wavelets, statistics},
pubstate = {published},
tppubtype = {inproceedings}
}
Crosskey, Miles C; Maggioni, Mauro
Learning of intrinsically low-dimensional stochastic systems in high-dimensions, I Technical Report
2013, (in preparation).
BibTeX | Tags: Manifold Learning, stochastic systems
@techreport{CM:MultiscaleDynamicsI,
title = {Learning of intrinsically low-dimensional stochastic systems in high-dimensions, I},
author = {Miles C Crosskey and Mauro Maggioni},
year = {2013},
date = {2013-01-01},
note = {in preparation},
keywords = {Manifold Learning, stochastic systems},
pubstate = {published},
tppubtype = {techreport}
}
Gerber, S; Maggioni, Mauro
Multiscale dictionaries, transforms, and learning in high-dimensions Proceedings Article
In: Proc. SPIE conference Optics and Photonics, 2013.
BibTeX | Tags: dictionary learning, imaging, Machine learning, multiscale analysis
@inproceedings{GM_MultiscaleDictionariesSPIE,
title = {Multiscale dictionaries, transforms, and learning in high-dimensions},
author = {S Gerber and Mauro Maggioni},
year = {2013},
date = {2013-01-01},
booktitle = {Proc. SPIE conference Optics and Photonics},
keywords = {dictionary learning, imaging, Machine learning, multiscale analysis},
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.