Randomized Numerical Linear Algebra (RandNLA) is an area which uses randomness,
most notably random sampling and random projection methods, to develop improved …

Sampling methods that choose a subset of the data proportional to its diversity in the feature
space are popular for data summarization. However, recent studies have noted the …

In this paper, we present a feature fusion method designed for the Gaussian process
model's kernel functions for the probabilistic long-term load forecasting. To enrich the …

We study the complexity of sampling from a distribution over all index subsets of the set {1,...,
n} with the probability of a subset S proportional to the determinant of the submatrix LS of …

We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While
classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a …

The Nystrom method has long been popular for scaling up kernel methods. Its theoretical
guarantees and empirical performance rely critically on the quality of the landmarks …

Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic
model of set diversity. They are useful for a number of subset selection tasks, including …

Abstract Design researchers have struggled to produce quantitative predictions for exactly
why and when diversity might help or hinder design search efforts. This paper addresses …

Determinantal point processes (DPPs) are a useful probabilistic model for selecting a small
diverse subset out of a large collection of items, with applications in summarization …

Given a set of $ n $ vectors in $\mathbb {R}^ d $, the goal of the\emph {determinant
maximization} problem is to pick $ k $ vectors with the maximum volume. Determinant …