b'Jie Ding'

research

∙ 09/07/2023

Second-order, Positive, and Unconditional Energy Dissipative Scheme for Modified Poisson-Nernst-Planck Equations

First-order energy dissipative schemes in time are available in literatu...

0 Jie Ding, et al. ∙

research

∙ 06/08/2023

Robust Quickest Change Detection for Unnormalized Models

Detecting an abrupt and persistent change in the underlying distribution...

0 Suya Wu, et al. ∙

research

∙ 05/26/2023

A Framework for Incentivized Collaborative Learning

Collaborations among various entities, such as companies, research labs,...

0 Xinran Wang, et al. ∙

research

∙ 05/07/2023

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization

LASSO regularization is a popular regression tool to enhance the predict...

0 Gen Li, et al. ∙

research

∙ 04/18/2023

Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging

Federated Learning (FL) aims to train a machine learning (ML) model in a...

0 Erum Mushtaq, et al. ∙

research

∙ 04/15/2023

PI-FL: Personalized and Incentivized Federated Learning

Personalized FL has been widely used to cater to heterogeneity challenge...

0 Ahmad Faraz Khan, et al. ∙

research

∙ 02/11/2023

Pruning Deep Neural Networks from a Sparsity Perspective

In recent years, deep network pruning has attracted significant attentio...

0 Enmao Diao, et al. ∙

research

∙ 02/01/2023

Quickest Change Detection for Unnormalized Statistical Models

Classical quickest change detection algorithms require modeling pre-chan...

0 Suya Wu, et al. ∙

research

∙ 12/17/2022

Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders

Recommender Systems (RSs) have become increasingly important in many app...

0 Qi Le, et al. ∙

research

∙ 11/17/2022

Asymptotic behaviour of a conservative reaction-diffusion system associated with a Markov process algebra model

This paper demonstrates a lower and upper solution method to investigate...

0 Jie Ding, et al. ∙

research

∙ 06/23/2022

A Framework for Understanding Model Extraction Attack and Defense

The privacy of machine learning models has become a significant concern ...

0 Xun Xian, et al. ∙

research

∙ 06/11/2022

A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation

The goal of model compression is to reduce the size of a large neural ne...

0 Wenjing Yang, et al. ∙

research

∙ 02/22/2022

Convergence Analysis of Structure-Preserving Numerical Methods Based on Slotboom Transformation for the Poisson–Nernst–Planck Equations

The analysis of structure-preserving numerical methods for the Poisson–N...

0 Jie Ding, et al. ∙

research

∙ 02/05/2022

Asymptotic Critical Radii in Random Geometric Graphs over 3-Dimensional Convex regions

This article presents the precise asymptotical distribution of two types...

0 Jie Ding, et al. ∙

research

∙ 01/26/2022

On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections

We propose a new structured pruning framework for compressing Deep Neura...

0 Mohammadreza Soltani, et al. ∙

research

∙ 01/21/2022

Asymptotic Critical Transmission Radii in Wireless Networks over a Convex Region

Critical transmission ranges (or radii) in wireless ad-hoc and sensor ne...

0 Jie Ding, et al. ∙

research

∙ 12/27/2021

SPIDER: Searching Personalized Neural Architecture for Federated Learning

Federated learning (FL) is an efficient learning framework that assists ...

10 Erum Mushtaq, et al. ∙

research

∙ 11/25/2021

Characteristic Neural Ordinary Differential Equations

We propose Characteristic Neural Ordinary Differential Equations (C-NODE...

6 Xingzi Xu, et al. ∙

research

∙ 10/26/2021

Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders

A long-standing challenge in Recommender Systems (RCs) is the data spars...

0 Enmao Diao, et al. ∙

research

∙ 09/20/2021

Assisted Learning for Organizations with Limited Data

We develop an assisted learning framework for assisting organization-lev...

0 Cheng Chen, et al. ∙

research

∙ 09/14/2021

Targeted Cross-Validation

In many applications, we have access to the complete dataset but are onl...

0 Jiawei Zhang, et al. ∙

research

∙ 07/02/2021

Subset Privacy: Draw from an Obfuscated Urn

With the rapidly increasing ability to collect and analyze personal data...

0 Ganghua Wang, et al. ∙

research

∙ 06/22/2021

The Rate of Convergence of Variation-Constrained Deep Neural Networks

Multi-layer feedforward networks have been used to approximate a wide ra...

0 Gen Li, et al. ∙

research

∙ 06/17/2021

Interval Privacy: A Framework for Data Collection

The emerging public awareness and government regulations of data privacy...

0 Jie Ding, et al. ∙

research

∙ 06/02/2021

SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients

Federated Learning allows training machine learning models by using the ...

0 Enmao Diao, et al. ∙

research

∙ 06/02/2021

Gradient Assisted Learning

In distributed settings, collaborations between different entities, such...

0 Enmao Diao, et al. ∙

research

∙ 03/18/2021

Learning Time Series from Scale Information

Sequentially obtained dataset usually exhibits different behavior at dif...

0 Yuan Yang, et al. ∙

research

∙ 12/24/2020

On Statistical Efficiency in Learning

A central issue of many statistical learning problems is to select an ap...

0 Jie Ding, et al. ∙

research

∙ 12/18/2020

Grant-Free Random Access in Machine-Type Communication: Approaches and Challenges

Massive machine-type communication (MTC) is expected to play a key role ...

0 Jinho Choi, et al. ∙

research

∙ 10/21/2020

ASCII: ASsisted Classification with Ignorance Interchange

The rapid development in data collecting devices and computation platfor...

0 Jiaying Zhou, et al. ∙

research

∙ 10/03/2020

HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Federated Learning (FL) is a method of training machine learning models ...

0 Enmao Diao, et al. ∙

research

∙ 10/02/2020

The Efficacy of L_1 Regularization in Two-Layer Neural Networks

A crucial problem in neural networks is to select the most appropriate n...

0 Gen Li, et al. ∙

research

∙ 09/27/2020

Large Deviation Principle for the Whittaker 2d Growth Model

The Whittaker 2d growth model is a triangular continuous Markov diffusio...

0 Jun Gao, et al. ∙

research

∙ 09/17/2020

Hysteresis and Linear Stability Analysis on Multiple Steady-State Solutions to the Poisson–Nernst–Planck equations with Steric Interactions

In this work, we numerically study linear stability of multiple steady-s...

0 Jie Ding, et al. ∙

research

∙ 09/13/2020

Information Laundering for Model Privacy

In this work, we propose information laundering, a novel framework for e...

12 Xinran Wang, et al. ∙

research

∙ 08/30/2020

Imitation Privacy

In recent years, there have been many cloud-based machine learning servi...

0 Xun Xian, et al. ∙

research

∙ 08/27/2020

Forecasting with Multiple Seasonality

An emerging number of modern applications involve forecasting time serie...

0 Tianyang Xie, et al. ∙

research

∙ 07/17/2020

Towards Enabling Critical mMTC: A Review of URLLC within mMTC

Massive machine-type communication (mMTC) and ultra-reliable and low-lat...

0 Shiva Raj Pokhrel, et al. ∙

research

∙ 07/12/2020

Fisher Auto-Encoders

It has been conjectured that the Fisher divergence is more robust to mod...

43 Khalil Elkhalil, et al. ∙

research

∙ 05/29/2020

Meta Clustering for Collaborative Learning

An emerging number of learning scenarios involve a set of learners/analy...

0 Chenglong Ye, et al. ∙

research

∙ 05/15/2020

Model Linkage Selection for Cooperative Learning

Rapid developments in data collecting devices and computation platforms ...

0 Jiaying Zhou, et al. ∙

research

∙ 04/01/2020

Assisted Learning and Imitation Privacy

Motivated by the emerging needs of decentralized learners with personali...

0 Xun Xian, et al. ∙

research

∙ 02/19/2020

Structure-Preserving and Efficient Numerical Methods for Ion Transport

Ion transport, often described by the Poisson–Nernst–Planck (PNP) equati...

0 Jie Ding, et al. ∙

research

∙ 02/07/2020

Multimodal Controller for Generative Models

Class-conditional generative models are crucial tools for data generatio...

0 Enmao Diao, et al. ∙

research

∙ 11/08/2019

A Binary Regression Adaptive Goodness-of-fit Test (BAGofT)

The Pearson's χ^2 test and residual deviance test are two classical good...

24 Jiawei Zhang, et al. ∙

research

∙ 11/03/2019

Variable Grouping Based Bayesian Additive Regression Tree

Using ensemble methods for regression has been a large success in obtain...

0 Yuhao Su, et al. ∙

research

∙ 10/23/2019

Deep Clustering of Compressed Variational Embeddings

Motivated by the ever-increasing demands for limited communication bandw...

0 Suya Wu, et al. ∙

research

∙ 10/21/2019

Perception-Distortion Trade-off with Restricted Boltzmann Machines

In this work, we introduce a new procedure for applying Restricted Boltz...

0 Chris Cannella, et al. ∙

research

∙ 10/20/2019

Speech Emotion Recognition with Dual-Sequence LSTM Architecture

Speech Emotion Recognition (SER) has emerged as a critical component of ...

0 Jianyou Wang, et al. ∙

research

∙ 08/21/2019

Restricted Recurrent Neural Networks

Recurrent Neural Network (RNN) and its variations such as Long Short-Ter...

0 Enmao Diao, et al. ∙

Jie Ding

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