Search Results for author:
Found 6 papers, 3 papers with code
Turnstile $\ell_p$ leverage score sampling with applications
When combined with preconditioning techniques, our algorithm extends to $\ell_p$ leverage score sampling over turnstile data streams.
Optimal bounds for $\ell_p$ sensitivity sampling via $\ell_2$ augmentation
As an application of our main result, we also obtain an $\tilde O(\varepsilon^{-2}\mu d)$ sensitivity sampling bound for logistic regression, where $\mu$ is a natural complexity measure for this problem.
Almost Linear Constant-Factor Sketching for $\ell_1$ and Logistic Regression
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.
Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis
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
We study the $p$-generalized probit regression model, which is a generalized linear model for binary responses.
Oblivious sketching for logistic regression
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.
