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no code implementations • 5 Apr 2023 • Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff

For this problem, under the $\ell_2$ norm, we observe an upper bound of $O(k \log (d)/\varepsilon + k\log(k/\varepsilon)/\varepsilon^2)$ rows, showing that sparse recovery is strictly easier to sketch than sparse regression.

1 code implementation • 31 Mar 2023 • Alexander Munteanu, Simon Omlor, David Woodruff

We improve upon previous oblivious sketching and turnstile streaming results for $\ell_1$ and logistic regression, giving a much smaller sketching dimension achieving $O(1)$-approximation and yielding an efficient optimization problem in the sketch space.

no code implementations • 26 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.

1 code implementation • 25 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.

1 code implementation • 14 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.

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.

no code implementations • 28 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.

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.

no code implementations • 9 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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