Search Results for author: Alexander Munteanu

Found 7 papers, 2 papers with code

Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis

no code implementations26 Jun 2022 Alexander Munteanu, Simon Omlor, Zhao Song, David P. Woodruff

A common method in training neural networks is to initialize all the weights to be independent Gaussian vectors.

$p$-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets

1 code implementation25 Mar 2022 Alexander Munteanu, Simon Omlor, Christian Peters

We study the $p$-generalized probit regression model, which is a generalized linear model for binary responses.

Oblivious sketching for logistic regression

1 code implementation14 Jul 2021 Alexander Munteanu, Simon Omlor, David Woodruff

Our sketch can be computed in input sparsity time over a turnstile data stream and reduces the size of a $d$-dimensional data set from $n$ to only $\operatorname{poly}(\mu d\log n)$ weighted points, where $\mu$ is a useful parameter which captures the complexity of compressing the data.

Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves

no code implementations NeurIPS 2019 Stefan Meintrup, Alexander Munteanu, Dennis Rohde

We study the $k$-median clustering problem for high-dimensional polygonal curves with finite but unbounded number of vertices.

Probabilistic smallest enclosing ball in high dimensions via subgradient sampling

no code implementations28 Feb 2019 Amer Krivošija, Alexander Munteanu

This is achieved via a novel combination of sampling techniques for clustering problems in metric spaces with the framework of stochastic subgradient descent.

On Coresets for Logistic Regression

no code implementations NeurIPS 2018 Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff

For data sets with bounded $\mu(X)$-complexity, we show that a novel sensitivity sampling scheme produces the first provably sublinear $(1\pm\varepsilon)$-coreset.

Coresets for Dependency Networks

no code implementations9 Oct 2017 Alejandro Molina, Alexander Munteanu, Kristian Kersting

Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables.

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