
Learning to Warm Up Cold Item Embeddings for Coldstart Recommendation with Meta Scaling and Shifting Networks
Recently, embedding techniques have achieved impressive success in recom...
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Using BART for Multiobjective Optimization of Noisy Multiple Objectives
Techniques to reduce the energy burden of an Industry 4.0 ecosystem ofte...
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Visualization of Covariance Structures for Multivariate SpatioTemporal Random Fields
The prevalence of multivariate spacetime data collected from monitoring...
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High Performance Multivariate Spatial Modeling for Geostatistical Data on Manycore Systems
Modeling and inferring spatial relationships and predicting missing valu...
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Collective Spectral Density Estimation and Clustering for SpatiallyCorrelated Data
In this paper, we develop a method for estimating and clustering twodim...
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Clustering Brain Signals: A Robust Approach Using Functional Data Ranking
In this paper, we analyze electroencephalograms (EEG) which are recordin...
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DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction
In spatial statistics, a common objective is to predict the values of a ...
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Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis
We study distributed composite optimization over networks: agents minimi...
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Distributed Algorithms for Composite Optimization: Unified and Tight Convergence Analysis
We study distributed composite optimization over networks: agents minimi...
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MultiSite HighFrequency Stochastic Precipitation Generator Using Censored SkewSymmetric Distributions
Due to improved measuring instruments, an accurate stochastic weather ge...
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Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces
In this paper, intelligent reflecting surfaces (IRSs) are employed to en...
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Efficiency Assessment of Approximated Spatial Predictions for Large Datasets
Due to the wellknown computational showstopper of the exact Maximum Lik...
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Semiparametric Estimation of Crosscovariance Functions for Multivariate Random Fields
The prevalence of spatially referenced multivariate data has impelled re...
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Estimation of Spatial Deformation for Nonstationary Processes via Variogram Alignment
In modeling spatial processes, a secondorder stationarity assumption is...
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Accelerated PrimalDual Algorithms for Distributed Smooth Convex Optimization over Networks
This paper proposes a novel family of primaldualbased distributed algo...
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A SemiParametric Estimation Method for the Quantile Spectrum with an Application to Earthquake Classification Using Convolutional Neural Network
In this paper, a new estimation method is introduced for the quantile sp...
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ExaGeoStatR: A Package for LargeScale Geostatistics in R
Parallel computing in Gaussian process calculation becomes a necessity f...
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Convergence Rate of Distributed Optimization Algorithms Based on Gradient Tracking
We study distributed, strongly convex and nonconvex, multiagent optimiza...
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Distributed Nonconvex Constrained Optimization over TimeVarying Digraphs
This paper considers nonconvex distributed constrained optimization over...
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Distributed BigData Optimization via Blockwise Gradient Tracking
We study distributed bigdata nonconvex optimization in multiagent netw...
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Distributed BigData Optimization via BlockIterative Gradient Tracking
We study distributed bigdata nonconvex optimization in multiagent netw...
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Decentralized Dictionary Learning Over TimeVarying Digraphs
This paper studies Dictionary Learning problems wherein the learning tas...
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Functional Outlier Detection and Taxonomy by Sequential Transformations
Functional data analysis can be seriously impaired by abnormal observati...
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Distributed BigData Optimization via Block Communications
We study distributed multiagent largescale optimization problems, wher...
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Parallel and Distributed Successive Convex Approximation Methods for BigData Optimization
Recent years have witnessed a surge of interest in parallel and distribu...
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Distributed BigData Optimization via BlockIterative Convexification and Averaging
In this paper, we study distributed bigdata nonconvex optimization in m...
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Parallel Approximation of the Maximum Likelihood Estimation for the Prediction of LargeScale Geostatistics Simulations
Maximum likelihood estimation is an important statistical technique for ...
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Tile LowRank Approximation of LargeScale Maximum Likelihood Estimation on Manycore Architectures
Maximum likelihood estimation is an important statistical technique for ...
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ASYSONATA: Achieving Geometric Convergence for Distributed Asynchronous Optimization
Can one obtain a geometrically convergent algorithm for distributed asyn...
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Coherencebased Time Series Clustering for Brain Connectivity Visualization
We develop the hierarchical cluster coherence (HCC) method for brain sig...
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Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets
We use available measurements to estimate the unknown parameters (varian...
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Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation
The problem of estimating sparse eigenvectors of a symmetric matrix attr...
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Robust Estimation of Structured Covariance Matrix for HeavyTailed Elliptical Distributions
This paper considers the problem of robustly estimating a structured cov...
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