Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Chhaya, Rachit"'
While coresets have been growing in terms of their application, barring few exceptions, they have mostly been limited to unsupervised settings. We consider supervised classification problems, and non-decomposable evaluation measures in such settings.
Externí odkaz:
http://arxiv.org/abs/2312.09885
We present algorithms that create coresets in an online setting for clustering problems according to a wide subset of Bregman divergences. Notably, our coresets have a small additive error, similar in magnitude to the lightweight coresets Bachem et.
Externí odkaz:
http://arxiv.org/abs/2012.06522
We study the effect of norm based regularization on the size of coresets for regression problems. Specifically, given a matrix $ \mathbf{A} \in {\mathbb{R}}^{n \times d}$ with $n\gg d$ and a vector $\mathbf{b} \in \mathbb{R} ^ n $ and $\lambda > 0$,
Externí odkaz:
http://arxiv.org/abs/2006.05440
Factorizing tensors has recently become an important optimization module in a number of machine learning pipelines, especially in latent variable models. We show how to do this efficiently in the streaming setting. Given a set of $n$ vectors, each in
Externí odkaz:
http://arxiv.org/abs/2006.01225
Autor:
Chhaya, Rachit
Publikováno v:
IndraStra Global.
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