Arvind K. Saibaba

74 papers C 1Journal 73

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Bayesian D-Optimal Experimental Designs via Column Subset Selection. SIAM J. Sci. Comput. Srinivas Eswar, Vishwas Rao, Arvind K. Saibaba
2026	J	`jnl`	FlexTrace: Exchangeable Randomized Trace Estimation for Matrix Functions. CoRR Madhusudan Madhavan, Alen Alexanderian, Arvind K. Saibaba
2026	J	`jnl`	Multifidelity sensor placement in Bayesian state estimation problems. CoRR Gabriela Ramon, Geena Sarnoski, Vasishta Tumuluri, Hugo Díaz, Arvind K. Saibaba
2026	J	`jnl`	Optimal Sensor Placement in Gaussian Processes via Column Subset Selection. CoRR Jessie Chen, Hangjie Ji, Arvind K. Saibaba
2025	J	`jnl`	A control-oriented approach to optimal sensor placement. CoRR Madhusudan Madhavan, Alen Alexanderian, Arvind K. Saibaba, Bart G. van Bloemen Waanders, Rebekah D. White
2025	J	`jnl`	A tensor-based dynamic mode decomposition based on the ⋆ CoRR Arvind K. Saibaba, Misha E. Kilmer, Khalil A Hall-Hooper, Fan Tian, Alex Mize
2025	J	`jnl`	Adaptive Randomized Tensor Train Rounding using Khatri-Rao Products. CoRR Hussam Al Daas, Grey Ballard, Laura Grigori, Mariana Martinez Aguilar, Arvind K. Saibaba, Bhisham Dev Verma
2025	J	`jnl`	Bridging the Gap Between Deterministic and Probabilistic Approaches to State Estimation. CoRR Lev Kakasenko, Alen Alexanderian, Mohammad Farazmand, Arvind K. Saibaba
2025	J	`jnl`	Improved Analysis of Khatri-Rao Random Projections and Applications. CoRR Arvind K. Saibaba, Bhisham Dev Verma, Grey Ballard
2025	J	`jnl`	Many (most?) column subset selection criteria are NP hard. CoRR Ilse C. F. Ipsen, Arvind K. Saibaba
2025	J	`jnl`	Optimal sensor placement under model uncertainty in the weak-constraint 4D-Var framework. CoRR Alen Alexanderian, Hugo Díaz, Vishwas Rao, Arvind K. Saibaba
2025	J	`jnl`	Parametric Hierarchical Matrix Approximations to Kernel Matrices. CoRR Abraham Khan, Chao Chen, Vishwas Rao, Arvind K. Saibaba
2025	J	`jnl`	Parametric Kernel Low-Rank Approximations Using Tensor Train Decomposition. SIAM J. Matrix Anal. Appl. Abraham Khan, Arvind K. Saibaba
2025	J	`jnl`	Randomized Low-Rank Approximations beyond Gaussian Random Matrices. SIAM J. Math. Data Sci. Arvind K. Saibaba, Agnieszka Miedlar
2025	J	`jnl`	Stable Rank and Intrinsic Dimension of Real and Complex Matrices. SIAM J. Matrix Anal. Appl. Ilse C. F. Ipsen, Arvind K. Saibaba
2025	J	`jnl`	Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory. CoRR Arvind K. Saibaba, Ilse C. F. Ipsen
2025	J	`jnl`	Structured Column Subset Selection for Bayesian Optimal Experimental Design. CoRR Hugo Díaz, Arvind K. Saibaba, Srinivas Eswar, Vishwas Rao, Zichao Wendy Di
2024	J	`jnl`	Bayesian D-Optimal Experimental Designs via Column Subset Selection: The Power of Reweighted Sensors. CoRR Srinivas Eswar, Vishwas Rao, Arvind K. Saibaba
2024	J	`jnl`	Efficient Algorithms for Bayesian Inverse Problems with Whittle-Matérn Priors. SIAM J. Sci. Comput. Harbir Antil, Arvind K. Saibaba
2024	J	`jnl`	Efficient hyperparameter estimation in Bayesian inverse problems using sample average approximation. CoRR Julianne Chung, Scot M. Miller, Malena Sabate Landman, Arvind K. Saibaba
2024	J	`jnl`	Efficient iterative methods for hyperparameter estimation in large-scale linear inverse problems. Adv. Comput. Math. Khalil A Hall-Hooper, Arvind K. Saibaba, Julianne Chung, Scot M. Miller
2024	J	`jnl`	Hybrid Projection Methods for Solution Decomposition in Large-Scale Bayesian Inverse Problems. SIAM J. Sci. Comput. Julianne Chung, Jiahua Jiang, Scot M. Miller, Arvind K. Saibaba
2024	J	`jnl`	Parametric kernel low-rank approximations using tensor train decomposition. CoRR Abraham Khan, Arvind K. Saibaba
2024	J	`jnl`	Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation. CoRR Amit N. Subrahmanya, Vishwas Rao, Arvind K. Saibaba
2024	J	`jnl`	Stable Rank and Intrinsic Dimension of Real and Complex Matrices. CoRR Ilse C. F. Ipsen, Arvind K. Saibaba
2023	J	`jnl`	Efficient iterative methods for hyperparameter estimation in large-scale linear inverse problems. CoRR Khalil A Hall-Hooper, Arvind K. Saibaba, Julianne Chung, Scot M. Miller
2023	J	`jnl`	Monte Carlo Methods for Estimating the Diagonal of a Real Symmetric Matrix. SIAM J. Matrix Anal. Appl. Eric Hallman, Ilse C. F. Ipsen, Arvind K. Saibaba
2023	J	`jnl`	Randomized Algorithms for Rounding in the Tensor-Train Format. SIAM J. Sci. Comput. Hussam Al Daas, Grey Ballard, Paul Cazeaux, Eric Hallman, Agnieszka Miedlar, Mirjeta Pasha, Tim W. Reid, Arvind K. Saibaba
2023	J	`jnl`	Randomized Reduced Basis Methods for Parameterized Fractional Elliptic PDEs. CoRR Harbir Antil, Arvind K. Saibaba
2023	J	`jnl`	Randomized low-rank approximations beyond Gaussian random matrices. CoRR Arvind K. Saibaba, Agnieszka Miedlar
2022	J	`jnl`	Efficient algorithms for Bayesian Inverse Problems with Whittle-Matérn Priors. CoRR Harbir Antil, Arvind K. Saibaba
2022	J	`jnl`	Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions. Adv. Comput. Math. Arvind K. Saibaba, Rachel Minster, Misha E. Kilmer
2022	J	`jnl`	Hybrid Projection Methods for Solution Decomposition in Large-scale Bayesian Inverse Problems. CoRR Julianne Chung, Jiahua Jiang, Scot M. Miller, Arvind K. Saibaba
2022	J	`jnl`	Kryging: geostatistical analysis of large-scale datasets using Krylov subspace methods. Stat. Comput. Suman Majumder, Yawen Guan, Brian J. Reich, Arvind K. Saibaba
2022	J	`jnl`	Monte Carlo Methods for Estimating the Diagonal of a Real Symmetric Matrix. CoRR Eric Hallman, Ilse C. F. Ipsen, Arvind K. Saibaba
2022	J	`jnl`	Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR). CoRR Ege Ozsar, Misha Elena Kilmer, Eric L. Miller, Eric de Sturler, Arvind K. Saibaba
2022	J	`jnl`	Robust Parameter Identifiability Analysis via Column Subset Selection. CoRR Katherine J. Pearce, Ilse C. F. Ipsen, Mansoor A. Haider, Arvind K. Saibaba, Ralph C. Smith
2022	J	`jnl`	Structured Matrix Approximations via Tensor Decompositions. SIAM J. Matrix Anal. Appl. Misha E. Kilmer, Arvind K. Saibaba
2022	J	`jnl`	Tensor-based flow reconstruction from optimally located sensor measurements. CoRR Mohammad Farazmand, Arvind K. Saibaba
2021	J	`jnl`	Approximating monomials using Chebyshev polynomials. CoRR Arvind K. Saibaba
2021	J	`jnl`	Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions. CoRR William Reese, Arvind K. Saibaba, Jonghyun Lee
2021	J	`jnl`	Efficient Algorithms for Eigensystem Realization Using Randomized SVD. SIAM J. Matrix Anal. Appl. Rachel Minster, Arvind K. Saibaba, Jishnudeep Kar, Aranya Chakrabortty
2021	J	`jnl`	Efficient edge-preserving methods for dynamic inverse problems. CoRR Mirjeta Pasha, Arvind K. Saibaba, Silvia Gazzola, Malena I. Español, Eric de Sturler
2021	J	`jnl`	Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions. CoRR Arvind K. Saibaba, Rachel Minster, Misha E. Kilmer
2021	J	`jnl`	Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis. Numer. Linear Algebra Appl. Arvind K. Saibaba, Joseph L. Hart, Bart G. van Bloemen Waanders
2021	J	`jnl`	Randomized algorithms for rounding in the Tensor-Train format. CoRR Hussam Al Daas, Grey Ballard, Paul Cazeaux, Eric Hallman, Agnieszka Miedlar, Mirjeta Pasha, Tim W. Reid, Arvind K. Saibaba
2021	J	`jnl`	Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems. J. Comput. Phys. Arvind K. Saibaba, Pranjal Prasad, Eric de Sturler, Eric L. Miller, Misha E. Kilmer
2021	J	`jnl`	Structured Matrix Approximations via Tensor Decompositions. CoRR Misha E. Kilmer, Arvind K. Saibaba
2020	J	`jnl`	Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems. Numer. Linear Algebra Appl. Arvind K. Saibaba, Julianne Chung, Katrina Petroske
2020	J	`jnl`	Efficient Randomized Algorithms for Subspace System Identification. CoRR Rachel Minster, Arvind K. Saibaba, Jishnudeep Kar, Aranya Chakrabortty
2020	J	`jnl`	Monte Carlo Estimators for the Schatten p-norm of Symmetric Positive Semidefinite Matrices. CoRR Ethan Dudley, Arvind K. Saibaba, Alen Alexanderian
2020	J	`jnl`	Randomization and Reweighted ℓ SIAM J. Sci. Comput. Elizabeth Herman, Alen Alexanderian, Arvind K. Saibaba
2020	J	`jnl`	Randomized Algorithms for Generalized Singular Value Decomposition with Application to Sensitivity Analysis. CoRR Arvind K. Saibaba, Joseph L. Hart, Bart G. van Bloemen Waanders
2020	J	`jnl`	Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format. SIAM J. Math. Data Sci. Rachel Minster, Arvind K. Saibaba, Misha E. Kilmer
2020	J	`jnl`	Randomized Discrete Empirical Interpolation Method for Nonlinear Model Reduction. SIAM J. Sci. Comput. Arvind K. Saibaba
2019	J	`jnl`	Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems. SIAM/ASA J. Uncertain. Quantification Arvind K. Saibaba, Johnathan M. Bardsley, D. Andrew Brown, Alen Alexanderian
2019	J	`jnl`	Randomization and reweighted 𝓁 CoRR Elizabeth Herman, Alen Alexanderian, Arvind K. Saibaba
2019	J	`jnl`	Randomized Subspace Iteration: Analysis of Canonical Angles and Unitarily Invariant Norms. SIAM J. Matrix Anal. Appl. Arvind K. Saibaba
2019	J	`jnl`	Randomized algorithms for low-rank tensor decompositions in the Tucker format. CoRR Rachel Minster, Arvind K. Saibaba, Misha E. Kilmer
2018	J	`jnl`	A randomized tensor singular value decomposition based on the t-product. Numer. Linear Algebra Appl. Jiani Zhang, Arvind K. Saibaba, Misha E. Kilmer, Shuchin Aeron
2018	J	`jnl`	Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems. SIAM J. Sci. Comput. Alen Alexanderian, Arvind K. Saibaba
2018	J	`jnl`	Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems. CoRR Ahmed Attia, Alen Alexanderian, Arvind K. Saibaba
2018	J	`jnl`	Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems. SIAM/ASA J. Uncertain. Quantification D. Andrew Brown, Arvind K. Saibaba, Sarah Vallélian
2017	J	`jnl`	Generalized Hybrid Iterative Methods for Large-Scale Bayesian Inverse Problems. SIAM J. Sci. Comput. Julianne Chung, Arvind K. Saibaba
2017	J	`jnl`	Multipreconditioned Gmres for Shifted Systems. SIAM J. Sci. Comput. Tania Bakhos, Peter K. Kitanidis, Scott Ladenheim, Arvind K. Saibaba, Daniel B. Szyld
2017	J	`jnl`	Randomized matrix-free trace and log-determinant estimators. Numerische Mathematik Arvind K. Saibaba, Alen Alexanderian, Ilse C. F. Ipsen
2016	J	`jnl`	A Randomized Tensor Singular Value Decomposition based on the t-product. CoRR Jiani Zhang, Arvind K. Saibaba, Misha Elena Kilmer, Shuchin Aeron
2016	J	`jnl`	HOID: Higher Order Interpolatory Decomposition for Tensors Based on Tucker Representation. SIAM J. Matrix Anal. Appl. Arvind K. Saibaba
2016	J	`jnl`	Randomized algorithms for generalized Hermitian eigenvalue problems with application to computing Karhunen-Loève expansion. Numer. Linear Algebra Appl. Arvind K. Saibaba, Jonghyun Lee, Peter K. Kitanidis
2015	J	`jnl`	A fast algorithm for parabolic PDE-based inverse problems based on Laplace transforms and flexible Krylov solvers. J. Comput. Phys. Tania Bakhos, Arvind K. Saibaba, Peter K. Kitanidis
2015	J	`jnl`	Fast Algorithms for Hyperspectral Diffuse Optical Tomography. SIAM J. Sci. Comput. Arvind K. Saibaba, Misha Elena Kilmer, Eric L. Miller, Sergio Fantini
2015	J	`jnl`	HOID: Higher Order Interpolatory Decomposition for tensors based on Tucker representation. CoRR Arvind K. Saibaba
2014	C	`conf`	A fast Kalman filter for time-lapse electrical resistivity tomography. IGARSS Arvind K. Saibaba, Eric L. Miller, Peter K. Kitanidis
2013	J	`jnl`	A Flexible Krylov Solver for Shifted Systems with Application to Oscillatory Hydraulic Tomography. SIAM J. Sci. Comput. Arvind K. Saibaba, Tania Bakhos, Peter K. Kitanidis