5 citations · 16 across the 20 of their papers we have counts for
14 papers · 1 filter
Entrywise Approximate Solutions for SDDM Systems in Almost-Linear Time
Angelo Farfan, Mehrdad Ghadiri, Junzhao Yang
We present an algorithm that given any invertible symmetric diagonally dominant M-matrix (SDDM), i.e., a principal submatrix of a graph Laplacian, and a n…
A Tight Lower Bound for the Approximation Guarantee of Higher-Order Singular Value Decomposition
Matthew Fahrbach, Mehrdad Ghadiri
We prove that the classic approximation guarantee for the higher-order singular value decomposition (HOSVD) is tight by constructing a tensor for which HOSVD achieves an approximat…
Entrywise Approximation for Matrix Inversion and Linear Systems
Mehrdad Ghadiri, Hoai-An Nguyen, Junzhao Yang
We study the bit complexity of inverting diagonally dominant matrices, which are associated with random walk quantities such as hitting times and escape probabilities. Such quantit…
Fast Tensor Completion via Approximate Richardson Iteration
Mehrdad Ghadiri, Matthew Fahrbach, Yunbum Kook +1
We study tensor completion (TC) through the lens of low-rank tensor decomposition (TD). Many TD algorithms use fast alternating minimization methods to solve highly structured line…
Entrywise Approximate Laplacian Solving
Jingbang Chen, Mehrdad Ghadiri, Hoai-An Nguyen +2
We study the escape probability problem in random walks over graphs. Given vertices, and , the problem asks for the probability that a random walk starting at will hi…
Improving the Bit Complexity of Communication for Distributed Convex Optimization
Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan +3
We consider the communication complexity of some fundamental convex optimization problems in the point-to-point (coordinator) and blackboard communication models. We strengthen kno…
The Bit Complexity of Efficient Continuous Optimization
Mehrdad Ghadiri, Richard Peng, Santosh S. Vempala
We analyze the bit complexity of efficient algorithms for fundamental optimization problems, such as linear regression, -norm regression, and linear programming (LP). State-of-t…
Approximately Optimal Core Shapes for Tensor Decompositions
Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu +1
This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition. We give…
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
Matthew Fahrbach, Thomas Fu, Mehrdad Ghadiri
Kronecker regression is a highly-structured least squares problem , where the design matrix $\mathbf{K} = \…
Constant-Factor Approximation Algorithms for Socially Fair -Clustering
Mehrdad Ghadiri, Mohit Singh, Santosh S. Vempala
We study approximation algorithms for the socially fair -clustering problem with groups, whose special cases include the socially fair -median () and socia…
Faster -Norm Regression Using Sparsity
Mehrdad Ghadiri, Richard Peng, Santosh S. Vempala
For a matrix with , we consider the dual problems of and $\min_{A^\top x=b} \|x\|_p^p,\, b\in \math…
Fast Low-Rank Tensor Decomposition by Ridge Leverage Score Sampling
Matthew Fahrbach, Mehrdad Ghadiri, Thomas Fu
Low-rank tensor decomposition generalizes low-rank matrix approximation and is a powerful technique for discovering low-dimensional structure in high-dimensional data. In this pape…
A Parameterized Family of Meta-Submodular Functions
Mehrdad Ghadiri, Richard Santiago, Bruce Shepherd
Submodular function maximization has found a wealth of new applications in machine learning models during the past years. The related supermodular maximization models (submodular m…
Beyond Submodular Maximization via One-Sided Smoothness
Mehrdad Ghadiri, Richard Santiago, Bruce Shepherd
The multilinear framework has achieved the breakthrough approximation for maximizing a monotone submodular function subject to a matroid constraint. This framework has a co…