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Found 14 papers, 6 papers with code
Data subsampling for Poisson regression with pth-root-link
In particular, the dependence on the number of input points can be reduced to polylogarithmic.
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.
Scalable Learning of Item Response Theory Models
Item Response Theory (IRT) models aim to assess latent abilities of $n$ examinees along with latent difficulty characteristics of $m$ test items from categorical data that indicates the quality of their corresponding answers.
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing.
Optimal Sketching Bounds for Sparse Linear Regression
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.
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.
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves
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
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
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
Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables.
