533 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- North Carolina State UniversityUS3 papers
- Arizona State UniversityUS2 papers
- United States Census BureauUS2 papers
- University of MissouriUS2 papers
- University of North Carolina at Chapel HillUS2 papers
- AgroParisTechFR1 paper
- Alex's Lemonade Stand FoundationUS1 paper
- Amazon (United States)US1 paper
- BC Children's Hospital Research InstituteCA1 paper
- Brigham and Women's HospitalUS1 paper
- Budapest University of Technology and EconomicsHU1 paper
- California Institute of TechnologyUS1 paper
36 papers
Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis
Joseph Nyangon, Ruth Akintunde
Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volat…
Monitoring road infrastructures from satellite images in Greater Maputo
Arianna Burzacchi, Matteo Landrò, Simone Vantini
The information about pavement surface type is rarely available in road network databases of developing countries although it represents a cornerstone of the design of efficient mo…
Towards optimized actions in critical situations of soccer games with deep reinforcement learning
Pegah Rahimian, Afshin Oroojlooy, Laszlo Toka
Soccer is a sparse rewarding game: any smart or careless action in critical situations can change the result of the match. Therefore players, coaches, and scouts are all curious ab…
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
Majid Jahani, Mohammadreza Nazari, Rachael Tappenden +2
This work presents a new algorithm for empirical risk minimization. The algorithm bridges the gap between first- and second-order methods by computing a search direction that uses…
The importance of transparency and reproducibility in artificial intelligence research
Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny +17
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines…
Constrained Multi-Objective Optimization for Automated Machine Learning
Steven Gardner, Oleg Golovidov, Joshua Griffin +4
Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General…
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM concentration
Veronica J. Berrocal, Yawen Guan, Amanda Muyskens +4
A typical problem in air pollution epidemiology is exposure assessment for individuals for which health data are available. Due to the sparsity of monitoring sites and the limited…
A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain
Tao Wang
Traditional machine learning algorithms use data from databases that are mutable, and therefore the data cannot be fully trusted. Also, the machine learning process is difficult to…
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection
Zekun Xu, Deovrat Kakde, Arin Chaudhuri
In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failur…
A fast algorithm for computing distance correlation
Arin Chaudhuri, Wenhao Hu
Classical dependence measures such as Pearson correlation, Spearman's , and Kendall's can detect only monotonic or linear dependence. To overcome these limitations, Szekely…
A New SVDD-Based Multivariate Non-parametric Process Capability Index
Deovrat Kakde, Arin Chaudhuri, Diana Shaw
Process capability index (PCI) is a commonly used statistic to measure ability of a process to operate within the given specifications or to produce products which meet the require…
A Bayesian Semiparametric Jolly-Seber Model with Individual Heterogeneity: An Application to Migratory Mallards at Stopover
Guohui Wu, Scott H. Holan, Alexis Avril +1
We propose a Bayesian hierarchical Jolly-Seber model that can account for individual heterogeneity in departure and the dependence of arrival time on covariates. Additionally, our…
Latent Gaussian Count Time Series
Yisu Jia, Stefanos Kechagias, James Livsey +2
This paper develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distribution…
Multi-Task Learning with Incomplete Data for Healthcare
Xin J. Hunt, Saba Emrani, Ilknur Kaynar Kabul +1
Multi-task learning is a type of transfer learning that trains multiple tasks simultaneously and leverages the shared information between related tasks to improve the generalizatio…
Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations
Jennifer B. Erway, Joshua Griffin, Roummel F. Marcia +1
Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient d…
Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning
Patrick Koch, Oleg Golovidov, Steven Gardner +3
Machine learning applications often require hyperparameter tuning. The hyperparameters usually drive both the efficiency of the model training process and the resulting model quali…
Provable Convex Co-clustering of Tensors
Eric C. Chi, Brian R. Gaines, Will Wei Sun +2
Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not a…
A new bandwidth selection criterion for using SVDD to analyze hyperspectral data
Yuwei Liao, Deovrat Kakde, Arin Chaudhuri +3
This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular mach…
Interpolating Population Distributions using Public-use Data: An Application to Income Segregation using American Community Survey Data
Matthew Simpson, Scott H. Holan, Christopher K. Wikle +1
Income segregation measures the extent to which households choose to live near other households with similar incomes. Sociologists theorize that income segregation can exacerbate t…
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel
Hansi Jiang, Haoyu Wang, Wenhao Hu +2
Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of supp…
The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description
Arin Chaudhuri, Deovrat Kakde, Carol Sadek +2
Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the r…
Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data
Sergiy Peredriy, Deovrat Kakde, Arin Chaudhuri
Support Vector Data Description (SVDD) provides a useful approach to construct a description of multivariate data for single-class classification and outlier detection with various…
A Non-Parametric Control Chart For High Frequency Multivariate Data
Deovrat Kakde, Sergriy Peredriy, Arin Chaudhuri +1
Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD based K-chart was first introduced by Sun an…
Leveraging Unstructured Data to Detect Emerging Reliability Issues
Deovrat Kakde, Arin Chaudhuri
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text d…