37 citations · 77 across the 22 of their papers we have counts for
6 papers · 1 filter
Optimal Dimension-Free Sampling for Regularized Classification
Meysam Alishahi, Alexander Munteanu, Simon Omlor +1
We prove optimal sampling bounds achieving -relative error for a broad class of Lipschitz continuous classification loss functions under various regularization t…
Scalable Learning of Multivariate Distributions via Coresets
Zeyu Ding, Katja Ickstadt, Nadja Klein +2
Efficient and scalable non-parametric or semi-parametric regression analysis and density estimation are of crucial importance to the fields of statistics and machine learning. Howe…
Data subsampling for Poisson regression with pth-root-link
Han Cheng Lie, Alexander Munteanu
We develop and analyze data subsampling techniques for Poisson regression, the standard model for count data . In particular, we consider the Poisson generalized li…
Scalable Learning of Item Response Theory Models
Susanne Frick, Amer Krivošija, Alexander Munteanu
Item Response Theory (IRT) models aim to assess latent abilities of examinees along with latent difficulty characteristics of test items from categorical data that indicate…
Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis
Alexander Munteanu, Simon Omlor, Zhao Song +1
A common method in training neural networks is to initialize all the weights to be independent Gaussian vectors. We observe that by instead initializing the weights into independen…
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves
Stefan Meintrup, Alexander Munteanu, Dennis Rohde
We study the -median clustering problem for high-dimensional polygonal curves with finite but unbounded number of vertices. We tackle the computational issue that arises from th…