5 citations · 16 across the 20 of their papers we have counts for
26 papers
Weibull-Stationary Stochastic Differential Equations for Conditional Long-Horizon Wind Power Forecasting
Luca Di Persio, Mehrdad Ghadiri
We present a one-month-ahead conditional probabilistic framework for wind-power forecasting at ten-minute resolution. Monthly Weibull shape and scale parameters are estimated from…
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…
Unbiased Regression-Adjusted Estimation of Average Treatment Effects in Randomized Controlled Trials
Alberto Abadie, Mehrdad Ghadiri, Ali Jadbabaie +1
This article introduces a leave-one-out regression adjustment (LOORA) for estimating average treatment effects in randomized controlled trials. In finite samples, LOORA removes the…
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…
A Note on the Stability of the Sherman-Morrison-Woodbury Formula
Linkai Ma, Christos Boutsikas, Mehrdad Ghadiri +1
We study the numerical stability of the Sherman-Morrison-Woodbury (SMW) identity. Let and assume and both have full-column rank. We explore error bounds for…
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 Dynamic Algebraic Formulas and their Determinants
Emile Anand, Jan van den Brand, Mehrdad Ghadiri +1
Many iterative algorithms in optimization, computational geometry, computer algebra, and other areas of computer science require repeated computation of some algebraic expression w…
On Symmetric Factorizations of Hankel Matrices
Mehrdad Ghadiri
We present two conjectures regarding the running time of computing symmetric factorizations for a Hankel matrix and its inverse as $\mathbf{B}\mathbf…
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…
Socially Fair k-Means Clustering
Mehrdad Ghadiri, Samira Samadi, Santosh Vempala
We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (…
Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi, Adam Ścibior, Andreas Munk +9
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampli…
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets
Mehrdad Ghadiri, Mark Schmidt
There are many problems in machine learning and data mining which are equivalent to selecting a non-redundant, high "quality" set of objects. Recommender systems, feature selection…
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…
Minimizing the Total Movement for Movement to Independence Problem on a Line
Mehrdad Ghadiri, Sina Yazdanbod
Given a positive real value , a set of points along a line and a distance function , in the movement to independence problem, we wish to move the points to new positions…
Active Distance-Based Clustering using K-medoids
Mehrdad Ghadiri, Amin Aghaee, Mahdieh Soleymani Baghshah
k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with points i…