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Poisson subsampling-based estimation for growing-dimensional expectile regression in massive data
As an effective tool for data analysis, expectile regression is widely used in the fields of statistics, econometrics and finance. However, most...
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Model-free global likelihood subsampling for massive data
Most existing studies for subsampling heavily depend on a specified model. If the assumed model is not correct, the performance of the subsample may...
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Variational auto-encoder based Bayesian Poisson tensor factorization for sparse and imbalanced count data
Non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson–Gamma models can derive full...
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A resampling-based approach to share reference panels
For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference...
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Generalized linear models for massive data via doubly-sketching
Generalized linear models are a popular analytics tool with interpretable results and broad applicability, but require iterative estimation...
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FPGA-Integrated Bag of Little Bootstraps Accelerator for Approximate Database Query Processing
We propose a novel approach to an FPGA-based approximate query processing accelerator using the Bag of Little Bootstraps (BLB) algorithm. The BLB...
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Recognition of Running Gait of Track and Field Athletes Based on Convolutional Neural Network
With the continuous development of competitive sports, higher requirements have been put forward for the athletic level and technical movements of...
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Anonymous Communication and Shuffle Model in Federated Learning
In the previous two chapters, we have discussed how to keep privacy through encrypting content transported in federated learning, namely, encrypt the...
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Face Image Privacy Protection with Differential Private k-Anonymity
In this section, we present a novel face image privacy protection method with differential private k-anonymity, which can not only generate...
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Towards Depth Fusion into Object Detectors for Improved Benthic Species Classification
Coonamessett Farm Foundation (CFF) conducts one of the optical surveys of the sea scallop resource using a HabCam towed vehicle. The CFF HabCam v3...
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On estimating the structure factor of a point process, with applications to hyperuniformity
Hyperuniformity is the study of stationary point processes with a sub-Poisson variance in a large window. In other words, counting the points of a...
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Sticky PDMP samplers for sparse and local inference problems
We construct a new class of efficient Monte Carlo methods based on continuous-time piecewise deterministic Markov processes (PDMPs) suitable for...
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Automatic Zig-Zag sampling in practice
Novel Monte Carlo methods to generate samples from a target distribution, such as a posterior from a Bayesian analysis, have rapidly expanded in the...
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Information Scaling
One important property of natural image data that distinguishes vision from other sensory tasks such as speech recognition is that scale plays an...
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Upsampling 4D Point Clouds of Human Body via Adversarial Generation
Time varying sequences of 3D point clouds, or 4D point clouds, are acquired at an increasing pace in several applications (e.g., LiDAR in autonomous...
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3D-B2U: Self-supervised Fluorescent Image Sequences Denoising
Fluorescence imaging can reveal the spatiotemporal dynamics of life activities. However, fluorescence image data suffers from photon shot noise due...
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Provable randomized rounding for minimum-similarity diversification
When searching for information in a data collection, we are often interested not only in finding relevant items, but also in assembling a diverse...
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Improvements on scalable stochastic Bayesian inference methods for multivariate Hawkes process
Multivariate Hawkes Processes (MHPs) are a class of point processes that can account for complex temporal dynamics among event sequences. In this...
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The Effect of Noise and Brightness on Convolutional Deep Neural Networks
The classification performance of Convolutional Neural Networks (CNNs) can be hampered by several factors. Sensor noise is one of these nuisances. In...
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Advances in Differential Privacy and Differentially Private Machine Learning
There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and...