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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...
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06/08/2023
Robust Quickest Change Detection for Unnormalized Models
Detecting an abrupt and persistent change in the underlying distribution...
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05/26/2023
A Framework for Incentivized Collaborative Learning
Collaborations among various entities, such as companies, research labs,...
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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...
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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...
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04/15/2023
PI-FL: Personalized and Incentivized Federated Learning
Personalized FL has been widely used to cater to heterogeneity challenge...
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02/11/2023
Pruning Deep Neural Networks from a Sparsity Perspective
In recent years, deep network pruning has attracted significant attentio...
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02/01/2023
Quickest Change Detection for Unnormalized Statistical Models
Classical quickest change detection algorithms require modeling pre-chan...
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12/17/2022
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders
Recommender Systems (RSs) have become increasingly important in many app...
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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...
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06/23/2022
A Framework for Understanding Model Extraction Attack and Defense
The privacy of machine learning models has become a significant concern ...
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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...
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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...
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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...
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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...
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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...
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12/27/2021
SPIDER: Searching Personalized Neural Architecture for Federated Learning
Federated learning (FL) is an efficient learning framework that assists ...
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11/25/2021
Characteristic Neural Ordinary Differential Equations
We propose Characteristic Neural Ordinary Differential Equations (C-NODE...
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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...
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09/20/2021
Assisted Learning for Organizations with Limited Data
We develop an assisted learning framework for assisting organization-lev...
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09/14/2021
Targeted Cross-Validation
In many applications, we have access to the complete dataset but are onl...
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07/02/2021
Subset Privacy: Draw from an Obfuscated Urn
With the rapidly increasing ability to collect and analyze personal data...
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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...
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06/17/2021
Interval Privacy: A Framework for Data Collection
The emerging public awareness and government regulations of data privacy...
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06/02/2021
SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients
Federated Learning allows training machine learning models by using the ...
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06/02/2021
Gradient Assisted Learning
In distributed settings, collaborations between different entities, such...
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03/18/2021
Learning Time Series from Scale Information
Sequentially obtained dataset usually exhibits different behavior at dif...
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12/24/2020
On Statistical Efficiency in Learning
A central issue of many statistical learning problems is to select an ap...
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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 ...
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10/21/2020
ASCII: ASsisted Classification with Ignorance Interchange
The rapid development in data collecting devices and computation platfor...
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10/03/2020
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Federated Learning (FL) is a method of training machine learning models ...
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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...
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09/27/2020
Large Deviation Principle for the Whittaker 2d Growth Model
The Whittaker 2d growth model is a triangular continuous Markov diffusio...
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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...
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09/13/2020
Information Laundering for Model Privacy
In this work, we propose information laundering, a novel framework for e...
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08/30/2020
Imitation Privacy
In recent years, there have been many cloud-based machine learning servi...
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08/27/2020
Forecasting with Multiple Seasonality
An emerging number of modern applications involve forecasting time serie...
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07/17/2020
Towards Enabling Critical mMTC: A Review of URLLC within mMTC
Massive machine-type communication (mMTC) and ultra-reliable and low-lat...
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07/12/2020
Fisher Auto-Encoders
It has been conjectured that the Fisher divergence is more robust to mod...
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05/29/2020
Meta Clustering for Collaborative Learning
An emerging number of learning scenarios involve a set of learners/analy...
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05/15/2020
Model Linkage Selection for Cooperative Learning
Rapid developments in data collecting devices and computation platforms ...
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04/01/2020
Assisted Learning and Imitation Privacy
Motivated by the emerging needs of decentralized learners with personali...
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02/19/2020
Structure-Preserving and Efficient Numerical Methods for Ion Transport
Ion transport, often described by the Poisson–Nernst–Planck (PNP) equati...
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02/07/2020
Multimodal Controller for Generative Models
Class-conditional generative models are crucial tools for data generatio...
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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...
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11/03/2019
Variable Grouping Based Bayesian Additive Regression Tree
Using ensemble methods for regression has been a large success in obtain...
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10/23/2019
Deep Clustering of Compressed Variational Embeddings
Motivated by the ever-increasing demands for limited communication bandw...
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10/21/2019
Perception-Distortion Trade-off with Restricted Boltzmann Machines
In this work, we introduce a new procedure for applying Restricted Boltz...
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10/20/2019
Speech Emotion Recognition with Dual-Sequence LSTM Architecture
Speech Emotion Recognition (SER) has emerged as a critical component of ...
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08/21/2019