
SemiEmpirical Objective Functions for MCMC Proposal Optimization
We introduce and demonstrate a semiempirical procedure for determining ...
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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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Towards Explainable Convolutional Features for Music Audio Modeling
Audio signals are often represented as spectrograms and treated as 2D im...
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Neural Architecture Search From Fréchet Task Distance
We formulate a Fréchettype asymmetric distance between tasks based on F...
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Talaria: A Framework for Simulation of Permissioned Blockchains for Logistics and Beyond
In this paper, we present Talaria, a novel permissioned blockchain simul...
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Neural Architecture Search From Task Similarity Measure
In this paper, we propose a neural architecture search framework based o...
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Generative Archimedean Copulas
We propose a new generative modeling technique for learning multidimensi...
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Deep Extreme Value Copulas for Estimation and Sampling
We propose a new method for modeling the distribution function of high d...
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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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TaskAware Neural Architecture Search
The design of handcrafted neural networks requires a lot of time and res...
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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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An Interpretable Baseline for Time Series Classification Without Intensive Learning
Recent advances in time series classification have largely focused on me...
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Deep CrossSubject Mapping of Neural Activity
In this paper, we demonstrate that a neural decoder trained on neural ac...
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Projected Latent Markov Chain Monte Carlo: Conditional Inference with Normalizing Flows
We introduce Projected Latent Markov Chain Monte Carlo (PLMCMC), a tech...
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Fisher AutoEncoders
It has been conjectured that the Fisher divergence is more robust to mod...
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Learning latent stochastic differential equations with variational autoencoders
We present a method for learning latent stochastic differential equation...
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Model Linkage Selection for Cooperative Learning
Rapid developments in data collecting devices and computation platforms ...
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Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization
Various types of parameter restart schemes have been proposed for accele...
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Multimodal Controller for Generative Models
Classconditional generative models are crucial tools for data generatio...
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Robust Marine Buoy Placement for Ship Detection Using Dropout KMeans
Marine buoys aid in the battle against Illegal, Unreported and Unregulat...
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A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret
In this paper, we consider the problem of distributed online convex opti...
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Crosssubject Decoding of Eye Movement Goals from Local Field Potentials
Objective. We consider the crosssubject decoding problem from local fie...
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Deep Clustering of Compressed Variational Embeddings
Motivated by the everincreasing demands for limited communication bandw...
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Learning Partial Differential Equations from Data Using Neural Networks
We develop a framework for estimating unknown partial differential equat...
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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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Distributed Lossy Image Compression with Recurrent Networks
We propose a new architecture for distributed image compression from a g...
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Convergence Rate of Empirical Spectral Distribution of Random Matrices from Linear Codes
It is known that the empirical spectral distribution of random matrices ...
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Momentum Schemes with Stochastic Variance Reduction for Nonconvex Composite Optimization
Two new stochastic variancereduced algorithms named SARAH and SPIDER ha...
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Minimaxoptimal decoding of movement goals from local field potentials using complex spectral features
We consider the problem of predicting eye movement goals from local fiel...
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SGD Converges to Global Minimum in Deep Learning via Starconvex Path
Stochastic gradient descent (SGD) has been found to be surprisingly effe...
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SpiderBoost: A Class of Faster Variancereduced Algorithms for Nonconvex Optimization
There has been extensive research on developing stochastic variance redu...
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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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Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels
Nonlinear kernels can be approximated using finitedimensional feature m...
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Sequential Detection of Regime Changes in Neural Data
The problem of detecting changes in firing patterns in neural data is st...
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Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using MultiModal Data
A framework is proposed to detect anomalies in multimodal data. A deep ...
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Stationary Geometric Graphical Model Selection
We consider the problem of model selection in Gaussian Markov fields in ...
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Sequential Event Detection Using Multimodal Data in Nonstationary Environments
The problem of sequential detection of anomalies in multimodal data is c...
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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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Region Detection in Markov Random Fields: Gaussian Case
In this work we consider the problem of model selection in Gaussian Mark...
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On DataDependent Random Features for Improved Generalization in Supervised Learning
The randomizedfeature approach has been successfully employed in large...
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On Optimal Generalizability in Parametric Learning
We consider the parametric learning problem, where the objective of the ...
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Wavelet Shrinkage and Thresholding based Robust Classification for Brain Computer Interface
A macaque monkey is trained to perform two different kinds of tasks, mem...
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Dictionary Learning and Sparse Codingbased Denoising for HighResolution Task Functional Connectivity MRI Analysis
We propose a novel denoising framework for task functional Magnetic Reso...
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Nonlinear Sequential Accepts and Rejects for Identification of Top Arms in Stochastic Bandits
We address the Mbestarm identification problem in multiarmed bandits....
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On Sequential Elimination Algorithms for BestArm Identification in MultiArmed Bandits
We consider the bestarm identification problem in multiarmed bandits, ...
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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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Vahid Tarokh
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Rhodes Family Professor of Electrical and Computer Engineering. Professor of Electrical and Computer Engineering. Professor of Computer Science (Secondary). Professor of Mathematics (Secondary).