
A unified framework for correlation mining in ultrahigh dimension
An important problem in large scale inference is the identification of v...
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OrthoReg: Robust Network Pruning Using Orthonormality Regularization
Network pruning in Convolutional Neural Networks (CNNs) has been extensi...
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PatternBased Analysis of Time Series: Estimation
While Internet of Things (IoT) devices and sensors create continuous str...
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Geometric Online Adaptation: GraphBased OSFS for Streaming Samples
Feature selection seeks a curated subset of available features such that...
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Testing that a Local Optimum of the Likelihood is Globally Optimum using Reparameterized Embeddings
Many mathematical imaging problems are posed as nonconvex optimization ...
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Geometric Estimation of Multivariate Dependency
This paper proposes a geometric estimator of dependency between a pair o...
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An unsupervised transfer learning algorithm for sleep monitoring
Objective: To develop multisensorwearabledevice sleep monitoring algor...
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Feature Selection for multilabeled variables via Dependency Maximization
Feature selection and reducing the dimensionality of data is an essentia...
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Learning to Bound the Multiclass Bayes Error
In the context of supervised learning, meta learning uses features, meta...
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Convergence Rates for Empirical Estimation of Binary Classification Bounds
Bounding the best achievable error probability for binary classification...
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PartLevel Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment
Pedestrians in videos have a wide range of appearances such as body pose...
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Parity Crowdsourcing for Cooperative Labeling
Consider a database of k objects, e.g., a set of videos, where each obje...
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Sequential Maximum Margin Classifiers for Partially Labeled Data
In many realworld applications, data is not collected as one batch, but...
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A DimensionIndependent discriminant between distributions
HenzePenrose divergence is a nonparametric divergence measure that can...
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Model Reduction in Chemical Reaction Networks: A DataDriven SparseLearning Approach
The reduction of large kinetic mechanisms is a crucial step for fluid dy...
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A DataDriven SparseLearning Approach to Model Reduction in Chemical Reaction Networks
In this paper, we propose an optimizationbased sparse learning approach...
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Fundamental Limits on Data Acquisition: Tradeoffs between Sample Complexity and Query Difficulty
In this paper, we consider querybased data acquisition and the correspo...
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Fast MetaLearning for Adaptive Hierarchical Classifier Design
We propose a new splitting criterion for a metalearning approach to mul...
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ZerothOrder Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications
In this paper, we design and analyze a new zerothorder online algorithm...
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Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms
Multilayer graphs are commonly used for representing different relations...
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Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity
We consider the problem of accelerating distributed optimization in mult...
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Direct estimation of density functionals using a polynomial basis
A number of fundamental quantities in statistical signal processing and ...
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Latent Laplacian Maximum Entropy Discrimination for Detection of HighUtility Anomalies
Datadriven anomaly detection methods suffer from the drawback of detect...
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Robust training on approximated minimalentropy set
In this paper, we propose a general framework to learn a robust largema...
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Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
One of the longstanding open problems in spectral graph clustering (SGC)...
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Multicentrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection
Many modern datasets can be represented as graphs and hence spectral dec...
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Incremental Method for Spectral Clustering of Increasing Orders
The smallest eigenvalues and the associated eigenvectors (i.e., eigenpai...
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Foundational principles for large scale inference: Illustrations through correlation mining
When can reliable inference be drawn in the "Big Data" context? This pap...
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A Dictionary Approach to EBSD Indexing
We propose a framework for indexing of grain and subgrain structures in...
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Empirically Estimable Classification Bounds Based on a New Divergence Measure
Information divergence functions play a critical role in statistics and ...
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Empirical nonparametric estimation of the Fisher Information
The Fisher information matrix (FIM) is a foundational concept in statist...
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Nonlinear unmixing of hyperspectral images: models and algorithms
When considering the problem of unmixing hyperspectral images, most of t...
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Variational Semiblind Sparse Deconvolution with Orthogonal Kernel Bases and its Application to MRFM
We present a variational Bayesian method of joint image reconstruction a...
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Semiblind Sparse Image Reconstruction with Application to MRFM
We propose a solution to the image deconvolution problem where the convo...
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Large Scale Correlation Screening
This paper treats the problem of screening for variables with high corre...
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Regularized LeastMeanSquare Algorithms
We consider adaptive system identification problems with convex constrai...
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Initialization Free Graph Based Clustering
This paper proposes an original approach to cluster multicomponent data...
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FINE: Fisher Information Nonparametric Embedding
We consider the problems of clustering, classification, and visualizatio...
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Alfred O. Hero
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Co Director of MIDAS and R. Jamison and Betty Williams Professor of Engineering at University of Michigan from 1984Present, Co Director, Michigan Institute for Data Science (MIDAS) at University of Michigan, R. Jamison and Betty Wiliams Professor of Engineering at University of Michigan, Chaired Professor at DIGITEO from 20082014, Director Division IX at IEEE from 20102011