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 …

In this paper, we propose scalable methods for maximizing a regularized submodular
function $ f\triangleq g-\ell $ expressed as the difference between a monotone submodular …

Gibbs sampling is a Markov chain Monte Carlo technique commonly used for estimating
marginal distributions. To speed up Gibbs sampling, there has recently been interest in …

We start with an overview of a class of submodular functions called SCMMs (sums of
concave composed with non-negative modular functions plus a final arbitrary modular). We …

Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples
variables from their conditional distributions. There are two common scan orders for the …

We study probability measures induced by set functions with constraints. Such measures
arise in a variety of real-world settings, where prior knowledge, resource limitations, or other …

In this work, we approach the problem of multi-task submodular optimization with the
perspective of local distributional robustness, within the neighborhood of a reference …

Modeling diversity of sets of items is important in many applications such as product
recommendation and data summarization. Probabilistic submodular models, a family of …

Strongly log-concave (SLC) distributions are a rich class of discrete probability distributions
over subsets of some ground set. They are strictly more general than strongly Rayleigh (SR) …

Strongly Rayleigh (SR) measures are discrete probability distributions over the subsets of a
ground set. They enjoy strong negative dependence properties, as a result of which they …