
GaussLegendre Features for Gaussian Process Regression
Gaussian processes provide a powerful probabilistic kernel learning fram...
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Quasi Monte Carlo TimeFrequency Analysis
We study signal processing tasks in which the signal is mapped via some ...
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Solving Trust Region Subproblems Using Riemannian Optimization
The Trust Region Subproblem is a fundamental optimization problem that t...
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Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning
The impressive performance exhibited by modern machine learning models h...
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Randomized Continuous Frames in TimeFrequency Analysis
Recently, a Monte Carlo approach was proposed for speeding up signal pro...
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Speeding up Linear Programming using Randomized Linear Algebra
Linear programming (LP) is an extremely useful tool and has been success...
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TensorTensor Products for Optimal Representation and Compression
In this era of big data, data analytics and machine learning, it is impe...
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Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs
Many irregular domains such as social networks, financial transactions, ...
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Polynomial Tensor Sketch for Elementwise Function of LowRank Matrix
This paper studies how to sketch elementwise functions of lowrank matr...
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Randomized Riemannian Preconditioning for Quadratically Constrained Problems
Optimization problem with quadratic equality constraints are prevalent i...
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A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms
Reconstructing continuous signals from a small number of discrete sample...
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Stable Tensor Neural Networks for Rapid Deep Learning
We propose a tensor neural network (tNN) framework that offers an excit...
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Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Random Fourier features is one of the most popular techniques for scalin...
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Sketching for Principal Component Regression
Principal component regression (PCR) is a useful method for regularizing...
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Optimizing Spectral Sums using Randomized Chebyshev Expansions
The trace of matrix functions, often called spectral sums, e.g., rank, l...
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Should You Derive, Or Let the Data Drive? An Optimization Framework for Hybrid FirstPrinciples DataDriven Modeling
Mathematical models are used extensively for diverse tasks including ana...
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Experimental Design for NonParametric Correction of Misspecified Dynamical Models
We consider a class of misspecified dynamical models where the governing...
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Hierarchically Compositional Kernels for Scalable Nonparametric Learning
We propose a novel class of kernels to alleviate the high computational ...
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QuasiMonte Carlo Feature Maps for ShiftInvariant Kernels
We consider the problem of improving the efficiency of randomized Fourie...
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Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization
We describe novel subgradient methods for a broad class of matrix optimi...
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