Search Results for author:
Found 35 papers, 7 papers with code
Fair Structure Learning in Heterogeneous Graphical Models
Certain demographics may be over-represented in some detected communities and under-represented in others.
Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models
The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features.
OrthoReg: Robust Network Pruning Using Orthonormality Regularization
To address this issue, we propose OrthoReg, a principled regularization strategy that enforces orthonormality on a network's filters to reduce inter-filter correlation, thereby allowing reliable, efficient determination of group importance estimates, improved trainability of pruned networks, and efficient, simultaneous pruning of large groups of filters.
Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes
$\textbf{Conclusion}$: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring.
Predicting solar flares with machine learning: investigating solar cycle dependence
A deep learning network, Long-Short Term Memory (LSTM) network, is used in this work to predict whether the maximum flare class an active region (AR) will produce in the next 24 hours is class $\Gamma$.
Solar and Stellar Astrophysics
A Geometric Approach to Online Streaming Feature Selection
In this work, firstly, we assert that OSFS's main assumption of having data from all the samples available at runtime is unrealistic and introduce a new setting where features and samples are streamed concurrently called OSFS with Streaming Samples (OSFS-SS).
Testing that a Local Optimum of the Likelihood is Globally Optimum using Reparameterized Embeddings
When the problem is formulated in terms of maximizing the likelihood function under a statistical model for the measurements, one can construct a statistical test that a local maximum is in fact the global maximum.
Geometric Estimation of Multivariate Dependency
This paper proposes a geometric estimator of dependency between a pair of multivariate samples.
Feature Selection for multi-labeled variables via Dependency Maximization
Feature selection and reducing the dimensionality of data is an essential step in data analysis.
Learning to Bound the Multi-class Bayes Error
Providing a tight bound on the BER that is also feasible to estimate has been a challenge.
Convergence Rates for Empirical Estimation of Binary Classification Bounds
We derive a bound on the convergence rate for the Friedman-Rafsky (FR) estimator of the HP-divergence, which is related to a multivariate runs statistic for testing between two distributions.
Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment
Pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, and there exists the proposal shift problem in pedestrian detection that causes the loss of body parts such as head and legs.
Ranked #25 on
Pedestrian Detection
on Caltech
Parity Queries for Binary Classification
We define the query difficulty $\bar{d}$ as the average size of the query subsets and the sample complexity $n$ as the minimum number of measurements required to attain a given recovery accuracy.
Sequential Maximum Margin Classifiers for Partially Labeled Data
In many real-world applications, data is not collected as one batch, but sequentially over time, and often it is not possible or desirable to wait until the data is completely gathered before analyzing it.
A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks
In this paper, we introduce a novel approach for the identification of the influential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique.
A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network.
Fast Meta-Learning for Adaptive Hierarchical Classifier Design
The proposed empirical estimates of the Bayes error rate are computed from the minimal spanning tree (MST) of the samples from each pair of classes.
Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications
In this paper, we design and analyze a new zeroth-order online algorithm, namely, the zeroth-order online alternating direction method of multipliers (ZOO-ADMM), which enjoys dual advantages of being gradient-free operation and employing the ADMM to accommodate complex structured regularizers.
Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks.
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity
Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis.
Direct estimation of density functionals using a polynomial basis
Typically, estimating these quantities requires complete knowledge of the underlying distribution followed by multi-dimensional integration.
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies
Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not.
Robust training on approximated minimal-entropy set
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set.
Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
Incremental Method for Spectral Clustering of Increasing Orders
The smallest eigenvalues and the associated eigenvectors (i. e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection.
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection
Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful.
Learning to classify with possible sensor failures
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set.
Foundational principles for large scale inference: Illustrations through correlation mining
Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed.
A Dictionary Approach to EBSD Indexing
We discretize the domain of the forward model onto a dense grid of Euler angles and for each measured pattern we identify the most similar patterns in the dictionary.
Empirically Estimable Classification Bounds Based on a New Divergence Measure
Information divergence functions play a critical role in statistics and information theory.
Empirical non-parametric estimation of the Fisher Information
Traditional approaches to estimating the FIM require estimating the probability distribution function (PDF), or its parameters, along with its gradient or Hessian.
Nonlinear unmixing of hyperspectral images: models and algorithms
When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).
Multi-criteria Anomaly Detection using Pareto Depth Analysis
In such a case, multiple criteria can be defined, and one can test for anomalies by scalarizing the multiple criteria by taking some linear combination of them.
Ensemble weighted kernel estimators for multivariate entropy estimation
In this paper, it is shown that for sufficiently smooth densities, an ensemble of kernel plug-in estimators can be combined via a weighted convex combination, such that the resulting weighted estimator has a superior parametric MSE rate of convergence of order $O(T^{-1})$.
Efficient anomaly detection using bipartite k-NN graphs
In this paper, we propose a novel bipartite k-nearest neighbor graph (BP-kNNG) anomaly detection scheme for estimating minimum volume sets.
