
Subset Privacy: Draw from an Obfuscated Urn
With the rapidly increasing ability to collect and analyze personal data...
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The Rate of Convergence of VariationConstrained Deep Neural Networks
Multilayer feedforward networks have been used to approximate a wide ra...
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Interval Privacy: A Framework for Data Collection
The emerging public awareness and government regulations of data privacy...
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SemiFL: Communication Efficient SemiSupervised Federated Learning with Unlabeled Clients
Federated Learning allows training machine learning models by using the ...
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Gradient Assisted Learning
In distributed settings, collaborations between different entities, such...
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Learning Time Series from Scale Information
Sequentially obtained dataset usually exhibits different behavior at dif...
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On Statistical Efficiency in Learning
A central issue of many statistical learning problems is to select an ap...
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GrantFree Random Access in MachineType Communication: Approaches and Challenges
Massive machinetype communication (MTC) is expected to play a key role ...
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ASCII: ASsisted Classification with Ignorance Interchange
The rapid development in data collecting devices and computation platfor...
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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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The Efficacy of L_1 Regularization in TwoLayer Neural Networks
A crucial problem in neural networks is to select the most appropriate n...
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Large Deviation Principle for the Whittaker 2d Growth Model
The Whittaker 2d growth model is a triangular continuous Markov diffusio...
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Hysteresis and Linear Stability Analysis on Multiple SteadyState Solutions to the Poisson–Nernst–Planck equations with Steric Interactions
In this work, we numerically study linear stability of multiple steadys...
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Information Laundering for Model Privacy
In this work, we propose information laundering, a novel framework for e...
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Imitation Privacy
In recent years, there have been many cloudbased machine learning servi...
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Forecasting with Multiple Seasonality
An emerging number of modern applications involve forecasting time serie...
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Towards Enabling Critical mMTC: A Review of URLLC within mMTC
Massive machinetype communication (mMTC) and ultrareliable and lowlat...
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Fisher AutoEncoders
It has been conjectured that the Fisher divergence is more robust to mod...
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Meta Clustering for Collaborative Learning
An emerging number of learning scenarios involve a set of learners/analy...
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Model Linkage Selection for Cooperative Learning
Rapid developments in data collecting devices and computation platforms ...
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Assisted Learning and Imitation Privacy
Motivated by the emerging needs of decentralized learners with personali...
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StructurePreserving and Efficient Numerical Methods for Ion Transport
Ion transport, often described by the Poisson–Nernst–Planck (PNP) equati...
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Multimodal Controller for Generative Models
Classconditional generative models are crucial tools for data generatio...
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A Binary Regression Adaptive Goodnessoffit Test (BAGofT)
The Pearson's χ^2 test and residual deviance test are two classical good...
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Variable Grouping Based Bayesian Additive Regression Tree
Using ensemble methods for regression has been a large success in obtain...
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Deep Clustering of Compressed Variational Embeddings
Motivated by the everincreasing demands for limited communication bandw...
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PerceptionDistortion Tradeoff with Restricted Boltzmann Machines
In this work, we introduce a new procedure for applying Restricted Boltz...
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Speech Emotion Recognition with DualSequence LSTM Architecture
Speech Emotion Recognition (SER) has emerged as a critical component of ...
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Restricted Recurrent Neural Networks
Recurrent Neural Network (RNN) and its variations such as Long ShortTer...
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Optimal Preamble Length for Spectral Efficiency in GrantFree RA with Massive MIMO
Grantfree random access (RA) with massive MIMO is a promising RA techni...
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Distributed Lossy Image Compression with Recurrent Networks
We propose a new architecture for distributed image compression from a g...
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Model Selection Techniques  An Overview
In the era of big data, analysts usually explore various statistical mod...
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Multiple Preambles for High Success Rate of GrantFree Random Access with Massive MIMO
Grantfree random access (RA) with massive MIMO is a promising RA techni...
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Virtual Carrier Sensing Based Random Access in Massive MIMO Systems
The 5th generation mobile communication systems aim to support massive a...
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Success Probability of GrantFree Random Access with Massive MIMO
Massive MIMO opens up new avenues for enabling highly efficient random a...
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Evolutionary Spectra Based on the Multitaper Method with Application to Stationarity Test
In this work, we propose a new inference procedure for understanding non...
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Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics
Using a proper model to characterize a time series is crucial in making ...
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Bridging AIC and BIC: a new criterion for autoregression
We introduce a new criterion to determine the order of an autoregressive...
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DataDriven Learning of the Number of States in MultiState Autoregressive Models
In this work, we consider the class of multistate autoregressive proces...
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Jie Ding
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