34 citations · 123 across the 18 of their papers we have counts for
18 papers
Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
Pu Ren, Rie Nakata, Maxime Lacour +9
Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer fro…
Efficient Leverage Score Sampling for Tensor Train Decomposition
Vivek Bharadwaj, Beheshteh T. Rakhshan, Osman Asif Malik +1
Tensor Train~(TT) decomposition is widely used in the machine learning and quantum physics communities as a popular tool to efficiently compress high-dimensional tensor data. In th…
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
Nuojin Cheng, Osman Asif Malik, Subhayan De +2
Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary objective in model validation. However, achieving this goal entails balancing the ne…
Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software
Riley Murray, James Demmel, Michael W. Mahoney +10
Randomized numerical linear algebra - RandNLA, for short - concerns the use of randomization as a resource to develop improved algorithms for large-scale linear algebra computation…
Construction of Hierarchically Semi-Separable matrix Representation using Adaptive Johnson-Lindenstrauss Sketching
Yotam Yaniv, Pieter Ghysels, Osman Asif Malik +2
We present an extension of an adaptive, partially matrix-free, Hierarchically Semi-Separable (HSS) matrix construction algorithm by Gorman et al. [SIAM J. Sci. Comput. 41(5), 2019]…
Fast Exact Leverage Score Sampling from Khatri-Rao Products with Applications to Tensor Decomposition
Vivek Bharadwaj, Osman Asif Malik, Riley Murray +3
We present a data structure to randomly sample rows from the Khatri-Rao product of several matrices according to the exact distribution of its leverage scores. Our proposed sampler…
Sampling-Based Decomposition Algorithms for Arbitrary Tensor Networks
Osman Asif Malik, Vivek Bharadwaj, Riley Murray
We show how to develop sampling-based alternating least squares (ALS) algorithms for decomposition of tensors into any tensor network (TN) format. Provided the TN format satisfies…
Distributed-Memory Randomized Algorithms for Sparse Tensor CP Decomposition
Vivek Bharadwaj, Osman Asif Malik, Riley Murray +2
Candecomp / PARAFAC (CP) decomposition, a generalization of the matrix singular value decomposition to higher-dimensional tensors, is a popular tool for analyzing multidimensional…
Quadrature Sampling of Parametric Models with Bi-fidelity Boosting
Nuojin Cheng, Osman Asif Malik, Yiming Xu +3
Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space explorat…
Fast algorithms for least square problems with Kronecker lower subsets
Osman Asif Malik, Yiming Xu, Nuojin Cheng +3
While leverage score sampling provides powerful tools for approximating solutions to large least squares problems, the cost of computing exact scores and sampling often prohibits p…
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees
Osman Asif Malik
Recent papers have developed alternating least squares (ALS) methods for CP and tensor ring decomposition with a per-iteration cost which is sublinear in the number of input tensor…
Superresolution photoacoustic tomography using random speckle illumination and second order moments
Osman Asif Malik, Venkatalakshmi Vyjayanthi Narumanchi, Stephen Becker +1
Idier et al. [IEEE Trans. Comput. Imaging 4(1), 2018] propose a method which achieves superresolution in the microscopy setting by leveraging random speckle illumination and knowle…
Binary matrix factorization on special purpose hardware
Osman Asif Malik, Hayato Ushijima-Mwesigwa, Arnab Roy +2
Many fundamental problems in data mining can be reduced to one or more NP-hard combinatorial optimization problems. Recent advances in novel technologies such as quantum and quantu…
A Sampling-Based Method for Tensor Ring Decomposition
Osman Asif Malik, Stephen Becker
We propose a sampling-based method for computing the tensor ring (TR) decomposition of a data tensor. The method uses leverage score sampled alternating least squares to fit the TR…
Guarantees for the Kronecker Fast Johnson-Lindenstrauss Transform Using a Coherence and Sampling Argument
Osman Asif Malik, Stephen Becker
In the recent paper [Jin, Kolda & Ward, arXiv:1909.04801], it is proved that the Kronecker fast Johnson-Lindenstrauss transform (KFJLT) is, in fact, a Johnson-Lindenstrauss transfo…
Dynamic Graph Convolutional Networks Using the Tensor M-Product
Osman Asif Malik, Shashanka Ubaru, Lior Horesh +2
Many irregular domains such as social networks, financial transactions, neuron connections, and natural language constructs are represented using graph structures. In recent years,…
Randomization of Approximate Bilinear Computation for Matrix Multiplication
Osman Asif Malik, Stephen Becker
We present a method for randomizing formulas for bilinear computation of matrix products. We consider the implications of such randomization when there are two sources of error: On…
Fast Randomized Matrix and Tensor Interpolative Decomposition Using CountSketch
Osman Asif Malik, Stephen Becker
We propose a new fast randomized algorithm for interpolative decomposition of matrices which utilizes CountSketch. We then extend this approach to the tensor interpolative decompos…