Subquadratic kronecker regression with applications to tensor decomposition
Kronecker regression is a highly-structured least squares problem $\min_ {\mathbf
{x}}\lVert\mathbf {K}\mathbf {x}-\mathbf {b}\rVert_ {2}^ 2$, where the design matrix $\mathbf …
{x}}\lVert\mathbf {K}\mathbf {x}-\mathbf {b}\rVert_ {2}^ 2$, where the design matrix $\mathbf …
Ridge regression: Structure, cross-validation, and sketching
S Liu, E Dobriban - arXiv preprint arXiv:1910.02373, 2019 - arxiv.org
We study the following three fundamental problems about ridge regression:(1) what is the
structure of the estimator?(2) how to correctly use cross-validation to choose the …
structure of the estimator?(2) how to correctly use cross-validation to choose the …
Discriminative regression with adaptive graph diffusion
In this article, we propose a new linear regression (LR)-based multiclass classification
method, called discriminative regression with adaptive graph diffusion (DRAGD). Different …
method, called discriminative regression with adaptive graph diffusion (DRAGD). Different …
Quantum-inspired algorithms from randomized numerical linear algebra
We create classical (non-quantum) dynamic data structures supporting queries for
recommender systems and least-squares regression that are comparable to their quantum …
recommender systems and least-squares regression that are comparable to their quantum …
Sparse approximations with interior point methods
Large-scale optimization problems that seek sparse solutions have become ubiquitous.
They are routinely solved with various specialized first-order methods. Although such …
They are routinely solved with various specialized first-order methods. Although such …
A novel sequential coreset method for gradient descent algorithms
A wide range of optimization problems arising in machine learning can be solved by
gradient descent algorithms, and a central question in this area is how to efficiently …
gradient descent algorithms, and a central question in this area is how to efficiently …
Effective dimension adaptive sketching methods for faster regularized least-squares optimization
We propose a new randomized algorithm for solving L2-regularized least-squares problems
based on sketching. We consider two of the most popular random embeddings, namely …
based on sketching. We consider two of the most popular random embeddings, namely …
Faster randomized interior point methods for tall/wide linear programs
Linear programming (LP) is an extremely useful tool which has been successfully applied to
solve various problems in a wide range of areas, including operations research …
solve various problems in a wide range of areas, including operations research …
Fast Computation of Zero-Forcing Precoding for Massive MIMO-OFDM Systems
As a simple and popular transmission scheme, zero-forcing (ZF) precoding can effectively
reap the great benefits of a multiple-input multiple-output orthogonal frequency division …
reap the great benefits of a multiple-input multiple-output orthogonal frequency division …
Sketching algorithms and lower bounds for ridge regression
P Kacham, D Woodruff - International Conference on …, 2022 - proceedings.mlr.press
We give a sketching-based iterative algorithm that computes a $1+\varepsilon $
approximate solution for the ridge regression problem $\min_x\| Ax-b\| _2^ 2+\lambda\| x …
approximate solution for the ridge regression problem $\min_x\| Ax-b\| _2^ 2+\lambda\| x …