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Found 10 papers, 3 papers with code
Coresets for Dependency Networks
Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables.
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.
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.
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.
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.
$p$-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.
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.
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.
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.
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.
