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.