3 citations · 10 across the 11 of their papers we have counts for
11 papers
Optimal Dimension-Free Sampling for Regularized Classification
Meysam Alishahi, Alexander Munteanu, Simon Omlor +1
We prove optimal sampling bounds achieving -relative error for a broad class of Lipschitz continuous classification loss functions under various regularization t…
Scalable Learning of Multivariate Distributions via Coresets
Zeyu Ding, Katja Ickstadt, Nadja Klein +2
Efficient and scalable non-parametric or semi-parametric regression analysis and density estimation are of crucial importance to the fields of statistics and machine learning. Howe…
Hardness of High-Dimensional Linear Classification
Alexander Munteanu, Simon Omlor, Jeff M. Phillips
We establish new exponential in dimension lower bounds for the Maximum Halfspace Discrepancy problem, which models linear classification. Both are fundamental problems in computati…
Turnstile leverage score sampling with applications
Alexander Munteanu, Simon Omlor
The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be…
Optimal bounds for sensitivity sampling via augmentation
Alexander Munteanu, Simon Omlor
Data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of…
Almost Linear Constant-Factor Sketching for and Logistic Regression
Alexander Munteanu, Simon Omlor, David Woodruff
We improve upon previous oblivious sketching and turnstile streaming results for and logistic regression, giving a much smaller sketching dimension achieving -approx…
Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis
Alexander Munteanu, Simon Omlor, Zhao Song +1
A common method in training neural networks is to initialize all the weights to be independent Gaussian vectors. We observe that by instead initializing the weights into independen…
-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets
Alexander Munteanu, Simon Omlor, Christian Peters
We study the -generalized probit regression model, which is a generalized linear model for binary responses. It extends the standard probit model by replacing its link function,…
Oblivious sketching for logistic regression
Alexander Munteanu, Simon Omlor, David Woodruff
What guarantees are possible for solving logistic regression in one pass over a data stream? To answer this question, we present the first data oblivious sketch for logistic regres…
Single Machine Batch Scheduling to Minimize the Weighted Number of Tardy Jobs
Danny Hermelin, Matthias Mnich, Simon Omlor
The scheduling problem takes as input a batch setup time and a set of jobs, each having a processing time, a release date, a weight, and a due date; t…
Scheduling with Non-Renewable Resources: Minimizing the Sum of Completion Times
Kristóf Bérczi, Tamás Király, Simon Omlor
The paper considers single-machine scheduling problems with a non-renewable resource. In this setting, we are given a set jobs, each of which is characterized by a processing time,…