Predict-and-recompute conjugate gradient variants. - Tyler Chen (CoRR, 2019)

Rounding random variables to finite precision. - Tyler Chen (CoRR, 2020)

Predict-and-Recompute Conjugate Gradient Variants. - Tyler Chen; Erin C. Carson (SIAM J. Sci. Comput., 2020)

Analysis of stochastic Lanczos quadrature for spectrum approximation. - Tyler Chen; Thomas Trogdon; Shashanka Ubaru (ICML, 2021)

On the Convergence Rate of Variants of the Conjugate Gradient Algorithm in Finite Precision Arithmetic. - Anne Greenbaum; Hexuan Liu; Tyler Chen (SIAM J. Sci. Comput., 2021)

Randomized matrix-free quadrature for spectrum and spectral sum approximation. - Tyler Chen; Thomas Trogdon; Shashanka Ubaru (CoRR, 2022)

Numerical computation of the equilibrium-reduced density matrix for strongly coupled open quantum systems. - Tyler Chen; Yu-Chen Cheng (CoRR, 2022)

On the fast convergence of minibatch heavy ball momentum. - Raghu Bollapragada; Tyler Chen; Rachel A. Ward (CoRR, 2022)

Error Bounds for Lanczos-Based Matrix Function Approximation. - Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (SIAM J. Matrix Anal. Appl., 2022)

Stability of the Lanczos algorithm on matrices with regular spectral distributions. - Tyler Chen; Thomas Trogdon (CoRR, 2023)

Near-Optimality Guarantees for Approximating Rational Matrix Functions by the Lanczos Method. - Noah Amsel; Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (CoRR, 2023)

Optimal Polynomial Approximation to Rational Matrix Functions Using the Arnoldi Algorithm. - Tyler Chen; Anne Greenbaum; Natalie Wellen (CoRR, 2023)

A spectrum adaptive kernel polynomial method. - Tyler Chen (CoRR, 2023)

Low-Memory Krylov Subspace Methods for Optimal Rational Matrix Function Approximation. - Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (SIAM J. Matrix Anal. Appl., 2023)

Krylov-Aware Stochastic Trace Estimation. - Tyler Chen; Eric Hallman (SIAM J. Matrix Anal. Appl., 2023)

Nearly Optimal Approximation of Matrix Functions by the Lanczos Method. - Noah Amsel; Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (NeurIPS, 2024)

Large-scale Outdoor Cell-free mMIMO Channel Measurement in an Urban Scenario at 3.5 GHz. - Yuning Zhang; Thomas Choi 001; Zihang Cheng; Issei Kanno; Masaaki Ito; Jorge Gomez-Ponce; Hussein Hammoud; Bowei Wu; Ashwani Pradhan; Kelvin Arana; Pramod Krishna; Tianyi Yang; et al. (VTC Fall, 2024)

Fixed-sparsity matrix approximation from matrix-vector products. - Noah Amsel; Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco (CoRR, 2024)

Near-optimal convergence of the full orthogonalization method. - Tyler Chen; Gérard Meurant (CoRR, 2024)

The Lanczos algorithm for matrix functions: a handbook for scientists. - Tyler Chen (CoRR, 2024)

GMRES, pseudospectra, and Crouzeix's conjecture for shifted and scaled Ginibre matrices. - Tyler Chen; Anne Greenbaum; Thomas Trogdon (Math. Comput., 2024)

Faster Randomized Partial Trace Estimation. - Tyler Chen; Robert Chen; Kevin Li; Skai Nzeuton; Yilu Pan; Yixin Wang (SIAM J. Sci. Comput., 2024)

Near-optimal hierarchical matrix approximation from matrix-vector products. - Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco; David Persson (SODA, 2025)

Randomized Matrix-Free Quadrature: Unified and Uniform Bounds for Stochastic Lanczos Quadrature and the Kernel Polynomial Method. - Tyler Chen; Thomas Trogdon; Shashanka Ubaru (SIAM J. Sci. Comput., 2025)

Preconditioning without a preconditioner: faster ridge-regression and Gaussian sampling with randomized block Krylov subspace methods. - Tyler Chen; Caroline Huber; Ethan Lin; Hajar Zaid (CoRR, 2025)

Randomized block-Krylov subspace methods for low-rank approximation of matrix functions. - David Persson; Tyler Chen; Christopher Musco (CoRR, 2025)

A posteriori error bounds for the block-Lanczos method for matrix function approximation. - Qichen Xu; Tyler Chen (Numer. Algorithms, 2025)

Provably faster randomized and quantum algorithms for k-means clustering via uniform sampling. - Tyler Chen; Archan Ray; Akshay Seshadri; Dylan Herman; Bao Bach; Pranav Deshpande; Abhishek Som; Niraj Kumar 005; Marco Pistoia (CoRR, 2025)

Quasi-optimal hierarchically semi-separable matrix approximation. - Noah Amsel; Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco; David Persson (CoRR, 2025)

GPU-Parallelizable Randomized Sketch-and-Precondition for Linear Regression using Sparse Sign Sketches. - Tyler Chen; Pradeep Niroula; Archan Ray; Pragna Subrahmanya; Marco Pistoia; Niraj Kumar 005 (CoRR, 2025)

A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values. - Tyler Chen; Akshay Seshadri; Mattia J. Villani; Pradeep Niroula; Shouvanik Chakrabarti; Archan Ray; Pranav Deshpande; Romina Yalovetzky; Marco Pistoia; Niraj Kumar 005 (CoRR, 2025)

Query Efficient Structured Matrix Learning. - Noah Amsel; Pratyush Avi; Tyler Chen; Feyza Duman Keles; Chinmay Hegde; Cameron Musco; Christopher Musco; David Persson (CoRR, 2025)