Abstract We consider Multiclass and Multilabel classification with extremely large number of
classes, of which only few are labeled to each instance. In such setting, standard methods …

Extreme Classification comprises multi-class or multi-label prediction where there is a large
number of classes, and is increasingly relevant to many real-world applications such as text …

Time series data analytics has been a problem of substantial interests for decades, and
Dynamic Time War** (DTW) has been the most widely adopted technique to measure …

Random feature methods have been successful in various machine learning tasks, are easy
to compute, and come with theoretical accuracy bounds. They serve as an alternative …

We provide (high probability) bounds on the condition number of random feature matrices. In
particular, we show that if the complexity ratio $\frac {N}{m} $ where $ N $ is the number of …

Kernel methods have been shown to be effective for many machine learning tasks such as
classification and regression. In particular, support vector machines with the Gaussian …

Sparse shrunk additive models and sparse random feature models have been developed
separately as methods to learn low-order functions, where there are few interactions …

The method of" random Fourier features (RFF)" has become a popular tool for approximating
the" radial basis function (RBF)" kernel. The variance of RFF is actually large. Interestingly …

We investigate how to train kernel approximation methods that generalize well under a
memory budget. Building on recent theoretical work, we define a measure of kernel …

Spectral clustering is one of the most effective clustering approaches that capture hidden
cluster structures in the data. However, it does not scale well to large-scale problems due to …