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06/22/2023
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Recent years have seen many successful applications of machine learning ...
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05/25/2023
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
Quantifying the uncertainty of quantities of interest (QoIs) from physic...
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11/09/2022
QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compression
We present a new convolution layer for deep learning architectures which...
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09/13/2022
Quadrature Sampling of Parametric Models with Bi-fidelity Boosting
Least squares regression is a ubiquitous tool for building emulators (a....
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09/13/2022
Fast Algorithms for Monotone Lower Subsets of Kronecker Least Squares Problems
Approximate solutions to large least squares problems can be computed ef...
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12/06/2021
On the computation of a non-parametric estimator by convex optimization
Estimation of linear functionals from observed data is an important task...
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04/07/2021
Approximate maximum likelihood estimators for linear regression with design matrix uncertainty
In this paper we consider regression problems subject to arbitrary noise...
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02/12/2021
Stochastic Gradient Langevin Dynamics with Variance Reduction
Stochastic gradient Langevin dynamics (SGLD) has gained the attention of...
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01/07/2021
Modeling massive multivariate spatial data with the basis graphical lasso
We propose a new modeling framework for highly multivariate spatial proc...
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10/16/2020
A Sampling Based Method for Tensor Ring Decomposition
We propose a sampling based method for computing the tensor ring (TR) de...
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07/21/2020
Spectral estimation from simulations via sketching
Sketching is a stochastic dimension reduction method that preserves geom...
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03/26/2020
Robust Least Squares for Quantized Data
In this paper we formulate and solve a robust least squares problem for ...
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02/10/2020
Locality-sensitive hashing in function spaces
We discuss the problem of performing similarity search over function spa...
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11/19/2019
Guarantees for the Kronecker Fast Johnson-Lindenstrauss Transform Using a Coherence and Sampling Argument
In the recent paper [Jin, Kolda Ward, arXiv:1909.04801], it is prove...
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10/17/2019
Optimization and Learning with Information Streams: Time-varying Algorithms and Applications
There is a growing cross-disciplinary effort in the broad domain of opti...
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07/25/2019
Safe Feature Elimination for Non-Negativity Constrained Convex Optimization
Inspired by recent work on safe feature elimination for 1-norm regulariz...
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07/12/2019
Resolvability of Hamming Graphs
A subset of vertices in a graph is called resolving when the geodesic di...
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05/17/2019
Randomization of Approximate Bilinear Computation for Matrix Multiplication
We present a method for randomizing a formula for bilinear computation o...
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03/10/2019
One-Pass Sparsified Gaussian Mixtures
We present a one-pass sparsified Gaussian mixture model (SGMM). Given P-...
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02/19/2019
Penalized basis models for very large spatial datasets
Many modern spatial models express the stochastic variation component as...
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01/29/2019
Fast Randomized Matrix and Tensor Interpolative Decomposition Using CountSketch
We propose a new fast randomized algorithm for interpolative decompositi...
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01/24/2019
Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions
We consider the problem of finding local minimizers in non-convex and no...
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04/17/2018
Efficient Solvers for Sparse Subspace Clustering
Sparse subspace clustering (SSC) is a popular method in machine learning...
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08/08/2017
Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects
The Nyström method is a popular technique for computing fixed-rank appro...
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12/20/2016
Randomized Clustered Nystrom for Large-Scale Kernel Machines
The Nystrom method has been popular for generating the low-rank approxim...
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08/26/2016
A Randomized Approach to Efficient Kernel Clustering
Kernel-based K-means clustering has gained popularity due to its simplic...
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03/01/2016
Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition
We focus on the robust principal component analysis (RPCA) problem, and ...
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10/31/2015
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means
We analyze a compression scheme for large data sets that randomly keeps ...
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10/16/2015
Robust Partially-Compressed Least-Squares
Randomized matrix compression techniques, such as the Johnson-Lindenstra...
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04/05/2015
Efficient Dictionary Learning via Very Sparse Random Projections
Performing signal processing tasks on compressive measurements of data h...
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11/04/2014
