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09/10/2023
Hutchinson's Estimator is Bad at Kronecker-Trace-Estimation
We study the problem of estimating the trace of a matrix 𝐀 that can only...
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09/19/2022
Faster Randomized Interior Point Methods for Tall/Wide Linear Programs
Linear programming (LP) is an extremely useful tool which has been succe...
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09/07/2022
Manifold Free Riemannian Optimization
Riemannian optimization is a principled framework for solving optimizati...
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02/25/2022
Near Optimal Reconstruction of Spherical Harmonic Expansions
We propose an algorithm for robust recovery of the spherical harmonic ex...
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02/10/2022
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
Learning data representations under uncertainty is an important task tha...
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02/07/2022
Random Gegenbauer Features for Scalable Kernel Methods
We propose efficient random features for approximating a new and rich cl...
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01/31/2022
Low-Rank Updates of Matrix Square Roots
Models in which the covariance matrix has the structure of a sparse matr...
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11/28/2021
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization
Precision medicine is a clinical approach for disease prevention, detect...
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06/22/2021
Faster Randomized Methods for Orthogonality Constrained Problems
Recent literature has advocated the use of randomized methods for accele...
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06/15/2021
Scaling Neural Tangent Kernels via Sketching and Random Features
The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely...
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04/12/2021
Semi-Infinite Linear Regression and Its Applications
Finite linear least squares is one of the core problems of numerical lin...
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04/03/2021
Random Features for the Neural Tangent Kernel
The Neural Tangent Kernel (NTK) has discovered connections between deep ...
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01/04/2021
Gauss-Legendre Features for Gaussian Process Regression
Gaussian processes provide a powerful probabilistic kernel learning fram...
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11/03/2020
Quasi Monte Carlo Time-Frequency Analysis
We study signal processing tasks in which the signal is mapped via some ...
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10/15/2020
Solving Trust Region Subproblems Using Riemannian Optimization
The Trust Region Subproblem is a fundamental optimization problem that t...
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09/27/2020
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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09/20/2020
Randomized Continuous Frames in Time-Frequency Analysis
Recently, a Monte Carlo approach was proposed for speeding up signal pro...
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03/18/2020
Speeding up Linear Programming using Randomized Linear Algebra
Linear programming (LP) is an extremely useful tool and has been success...
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12/31/2019
Tensor-Tensor Products for Optimal Representation and Compression
In this era of big data, data analytics and machine learning, it is impe...
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10/16/2019
Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs
Many irregular domains such as social networks, financial transactions, ...
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05/28/2019
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
This paper studies how to sketch element-wise functions of low-rank matr...
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02/05/2019
Randomized Riemannian Preconditioning for Quadratically Constrained Problems
Optimization problem with quadratic equality constraints are prevalent i...
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12/20/2018
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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11/15/2018
Stable Tensor Neural Networks for Rapid Deep Learning
We propose a tensor neural network (t-NN) framework that offers an excit...
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04/26/2018
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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03/07/2018
Sketching for Principal Component Regression
Principal component regression (PCR) is a useful method for regularizing...
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02/18/2018
Optimizing Spectral Sums using Randomized Chebyshev Expansions
The trace of matrix functions, often called spectral sums, e.g., rank, l...
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11/12/2017
Should You Derive, Or Let the Data Drive? An Optimization Framework for Hybrid First-Principles Data-Driven Modeling
Mathematical models are used extensively for diverse tasks including ana...
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05/02/2017
Experimental Design for Non-Parametric Correction of Misspecified Dynamical Models
We consider a class of misspecified dynamical models where the governing...
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08/02/2016
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
We propose a novel class of kernels to alleviate the high computational ...
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12/29/2014
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
We consider the problem of improving the efficiency of randomized Fourie...
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06/27/2012