In the time of Big Data, training complex models on large-scale data sets is challenging,
making it appealing to reduce data volume for saving computation resources by …

Subsampling is a common and often effective method to deal with the computational
challenges of large datasets. However, for most statistical models, there is no well-motivated …

Partial least squares (PLS) performs well for high-dimensional regression problems, where
the number of predictors can far exceed the number of observations. Similar to many other …

Rapid advance in science and technology in the past decade brings an extraordinary
amount of data, offering researchers an unprecedented opportunity to tackle complex …

Modern statistical analysis often encounters datasets with large sizes. For these datasets,
conventional estimation methods can hardly be used immediately because practitioners …

In this paper, we aim to enable both efficient and accurate approximations on arbitrary sub-
datasets of a large dataset. Due to the prohibitive storage overhead of caching offline …

A significant hurdle for analyzing large sample data is the lack of effective statistical
computing and inference methods. An emerging powerful approach for analyzing large …

Subsampling is an effective way to deal with big data problems and many subsampling
approaches have been proposed for different models, such as leverage sampling for linear …

Score-based generative models (SGMs) are a recent breakthrough in generating fake
images. SGMs are known to surpass other generative models, eg, generative adversarial …

Many machine learning tasks require sampling a subset of items from a collection based on
a parameterized distribution. The Gumbel-softmax trick can be used to sample a single item …