3 citations · 4 across the 7 of their papers we have counts for
8 papers
Optimizing Polymatroid Functions
Sungjin Im, Benjamin Moseley, Hung Q. Ngo +2
We consider a class of optimization problems that involve determining the maximum value that a function in a particular class can attain subject to a collection of difference const…
Relational Boosted Regression Trees
Sonia Cromp, Alireza Samadian, Kirk Pruhs
Many tasks use data housed in relational databases to train boosted regression tree models. In this paper, we give a relational adaptation of the greedy algorithm for training boos…
In-Database Regression in Input Sparsity Time
Rajesh Jayaram, Alireza Samadian, David P. Woodruff +1
Sketching is a powerful dimensionality reduction technique for accelerating algorithms for data analysis. A crucial step in sketching methods is to compute a subspace embedding (SE…
Instance Optimal Join Size Estimation
Mahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley +2
We consider the problem of efficiently estimating the size of the inner join of a collection of preprocessed relational tables from the perspective of instance optimality analysis.…
Relational Algorithms for k-means Clustering
Benjamin Moseley, Kirk Pruhs, Alireza Samadian +1
This paper gives a k-means approximation algorithm that is efficient in the relational algorithms model. This is an algorithm that operates directly on a relational database withou…
A Relational Gradient Descent Algorithm For Support Vector Machine Training
Mahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley +2
We consider gradient descent like algorithms for Support Vector Machine (SVM) training when the data is in relational form. The gradient of the SVM objective can not be efficiently…
Approximate Aggregate Queries Under Additive Inequalities
Mahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley +2
We consider the problem of evaluating certain types of functional aggregation queries on relational data subject to additive inequalities. Such aggregation queries, with a smallish…
On Coresets for Regularized Loss Minimization
Ryan R. Curtin, Sungjin Im, Ben Moseley +2
We design and mathematically analyze sampling-based algorithms for regularized loss minimization problems that are implementable in popular computational models for large data, in…