Search Results for author: Mauro Maggioni

Found 24 papers, 7 papers with code

Learning Transition Operators From Sparse Space-Time Samples

no code implementations1 Dec 2022 Christian Kümmerle, Mauro Maggioni, Sui Tang

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.

Low-Rank Matrix Completion

Learning Interaction Variables and Kernels from Observations of Agent-Based Systems

no code implementations4 Aug 2022 Jinchao Feng, Mauro Maggioni, Patrick Martin, Ming Zhong

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.

Dimensionality Reduction

Unsupervised learning of observation functions in state-space models by nonparametric moment methods

no code implementations12 Jul 2022 Qingci An, Yannis Kevrekidis, Fei Lu, Mauro Maggioni

Assuming abundant data of the observation process along with the distribution of the state process, we introduce a nonparametric generalized moment method to estimate the observation function via constrained regression.

Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides

no code implementations26 Aug 2021 Ming Zhong, Jason Miller, Mauro Maggioni

Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics.

BIG-bench Machine Learning

Nonlinear model reduction for slow-fast stochastic systems near manifolds

no code implementations5 Apr 2021 Felix X. -F. Ye, Sichen Yang, Mauro Maggioni

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.

Efficient Exploration

Learning Interaction Kernels for Agent Systems on Riemannian Manifolds

no code implementations30 Jan 2021 Mauro Maggioni, Jason Miller, Hongda Qiu, Ming Zhong

Interacting agent and particle systems are extensively used to model complex phenomena in science and engineering.

Multiscale regression on unknown manifolds

no code implementations13 Jan 2021 Wenjing Liao, Mauro Maggioni, Stefano Vigogna

We consider the regression problem of estimating functions on $\mathbb{R}^D$ but supported on a $d$-dimensional manifold $ \mathcal{M} \subset \mathbb{R}^D $ with $ d \ll D $.


Anatomically-Informed Deep Learning on Contrast-Enhanced Cardiac MRI for Scar Segmentation and Clinical Feature Extraction

no code implementations21 Oct 2020 Haley G. Abramson, Dan M. Popescu, Rebecca Yu, Changxin Lai, Julie K. Shade, Katherine C. Wu, Mauro Maggioni, Natalia A. Trayanova

Visualizing disease-induced scarring and fibrosis in the heart on cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramount in characterizing disease progression and quantifying pathophysiological substrates of arrhythmias.

Myocardium Segmentation

Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems

no code implementations8 Oct 2020 Jason Miller, Sui Tang, Ming Zhong, Mauro Maggioni

Modeling the complex interactions of systems of particles or agents is a fundamental scientific and mathematical problem that is studied in diverse fields, ranging from physics and biology, to economics and machine learning.

Learning Theory

Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories

no code implementations30 Jul 2020 Fei Lu, Mauro Maggioni, Sui Tang

Finally, we exhibit an efficient parallel algorithm 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.

Data-driven Discovery of Emergent Behaviors in Collective Dynamics

1 code implementation23 Dec 2019 Mauro Maggioni, Jason Miller, Ming Zhong

We consider the fundamental problem of inferring interaction kernels from observations of agent-based dynamical systems given observations of trajectories, in particular for collective dynamical systems exhibiting emergent behaviors with complicated interaction kernels, in a nonparametric fashion, and for kernels which are parametrized by a single unknown parameter.

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories

no code implementations10 Oct 2019 Fei Lu, Mauro Maggioni, Sui Tang

These simulations also suggest that our estimators are robust to noise in the observations, and produce accurate predictions of dynamics in relative large time intervals, even when they are learned from data collected in short time intervals.

Learning by Active Nonlinear Diffusion

no code implementations30 May 2019 Mauro Maggioni, James M. Murphy

This article proposes an active learning method for high dimensional data, based on intrinsic data geometries learned through diffusion processes on graphs.

Active Learning

Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering

no code implementations8 Feb 2019 James M. Murphy, Mauro Maggioni

The explicit incorporation of spatial regularity into the diffusion construction leads to smoother random processes that are more adapted for unsupervised machine learning than those based on spectra alone.

Density Estimation Image Clustering

Nonparametric inference of interaction laws in systems of agents from trajectory data

1 code implementation14 Dec 2018 Fei Lu, Mauro Maggioni, Sui Tang, Ming Zhong

Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines.

Learning by Unsupervised Nonlinear Diffusion

no code implementations15 Oct 2018 Mauro Maggioni, James M. Murphy

This paper proposes and analyzes a novel clustering algorithm that combines graph-based diffusion geometry with techniques based on density and mode estimation.

Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms

1 code implementation17 Dec 2017 Anna Little, Mauro Maggioni, James M. Murphy

We consider the problem of clustering with the longest-leg path distance (LLPD) metric, which is informative for elongated and irregularly shaped clusters.

Supervised Dimensionality Reduction for Big Data

1 code implementation5 Sep 2017 Joshua T. Vogelstein, Eric Bridgeford, Minh Tang, Da Zheng, Christopher Douville, Randal Burns, Mauro Maggioni

To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences.

General Classification Supervised dimensionality reduction

Multiscale Strategies for Computing Optimal Transport

2 code implementations8 Aug 2017 Samuel Gerber, Mauro Maggioni

The approach is based on an adaptive multiscale decomposition of the point sets.


Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion

no code implementations26 Apr 2017 James M. Murphy, Mauro Maggioni

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing.

Active Learning

Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data

no code implementations3 Nov 2016 Wenjing Liao, Mauro Maggioni

We consider the problem of efficiently approximating and encoding high-dimensional data sampled from a probability distribution $\rho$ in $\mathbb{R}^D$, that is nearly supported on a $d$-dimensional set $\mathcal{M}$ - for example supported on a $d$-dimensional Riemannian manifold.

Dictionary Learning

Discovering and Deciphering Relationships Across Disparate Data Modalities

4 code implementations16 Sep 2016 Joshua T. Vogelstein, Eric Bridgeford, Qing Wang, Carey E. Priebe, Mauro Maggioni, Cencheng Shen

Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly important in modern biological datasets.

High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies

no code implementations24 Sep 2015 Yi, Wang, Guangliang Chen, Mauro Maggioni

We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals.

Anomaly Detection

Sparse Projection Oblique Randomer Forests

2 code implementations10 Jun 2015 Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Jason Yim, Carey E. Priebe, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein

Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.

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