42 citations · 107 across the 12 of their papers we have counts for
14 papers
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
Spencer Giddens, Yiwang Zhou, Kevin R. Krull +3
Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides mathematically pro…
Supervised Homogeneity Fusion: a Combinatorial Approach
Wen Wang, Shihao Wu, Ziwei Zhu +2
Fusing regression coefficients into homogenous groups can unveil those coefficients that share a common value within each group. Such groupwise homogeneity reduces the intrinsic di…
Parallel-and-stream accelerator for computationally fast supervised learning
Emily C. Hector, Lan Luo, Peter X. -K. Song
Two dominant distributed computing strategies have emerged to overcome the computational bottleneck of supervised learning with big data: parallel data processing in the MapReduce…
Data Discovery Using Lossless Compression-Based Sparse Representation
Elyas Sabeti, Peter X. K. Song, Alfred O. Hero
Sparse representation has been widely used in data compression, signal and image denoising, dimensionality reduction and computer vision. While overcomplete dictionaries are requir…
Joint integrative analysis of multiple data sources with correlated vector outcomes
Emily C. Hector, Peter X. -K. Song
We propose a distributed quadratic inference function framework to jointly estimate regression parameters from multiple potentially heterogeneous data sources with correlated vecto…
Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes
Xichen She, Yaya Zhai, Ricardo Henao +5
: To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. : We i…
Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes
Emily C. Hector, Peter X. -K. Song
This paper presents a unified framework for supervised learning and inference procedures using the divide-and-conquer approach for high-dimensional correlated outcomes. We propose…
Pattern-Based Analysis of Time Series: Estimation
Elyas Sabeti, Peter X. K. Song, Alfred O. Hero
While Internet of Things (IoT) devices and sensors create continuous streams of information, Big Data infrastructures are deemed to handle the influx of data in real-time. One type…
A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis
Emily C. Hector, Peter X. -K. Song
This paper is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multi-level nested correlations.…
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
Fei Wang, Ling Zhou, Lu Tang +1
Simultaneous inference after model selection is of critical importance to address scientific hypotheses involving a set of parameters. In this paper, we consider high-dimensional l…
An unsupervised transfer learning algorithm for sleep monitoring
Xichen She, Yaya Zhai, Ricardo Henao +4
Objective: To develop multisensor-wearable-device sleep monitoring algorithms that are robust to health disruptions affecting sleep patterns. Methods: We develop an unsupervised tr…
Scalable and Efficient Statistical Inference with Estimating Functions in the MapReduce Paradigm for Big Data
Ling Zhou, Peter X. -K. Song
The theory of statistical inference along with the strategy of divide-and-conquer for large- scale data analysis has recently attracted considerable interest due to great popularit…
Distributed Simultaneous Inference in Generalized Linear Models via Confidence Distribution
Lu Tang, Ling Zhou, Peter X. -K. Song
We propose a distributed method for simultaneous inference for datasets with sample size much larger than the number of covariates, i.e., N >> p, in the generalized linear models f…
Signal extraction and breakpoint identification for array CGH data using robust state space model
Bin Zhu, Jeremy M. G. Taylor, Peter X. -K. Song
Array comparative genomic hybridization(CGH) is a high resolution technique to assess DNA copy number variation. Identifying breakpoints where copy number changes will enhance the…