The determinantal point process (DPP) is an elegant probabilistic model of repulsion with
applications in various machine learning tasks including summarization and search …

Many recommendation systems produce result sets with large numbers of highly similar
items. Diversifying these results is often accomplished with heuristics, which are …

The randomly pivoted Cholesky algorithm (RPCholesky) computes a factorized rank‐kk
approximation of an N× NN*N positive‐semidefinite (psd) matrix. RPCholesky requires only …

Relevant recommendation is a special recommendation scenario which provides relevant
items when users express interests on one target item (eg, click, like and purchase). Besides …

Determinantal point processes (DPPs) are specific probability distributions over clouds of
points that are used as models and computational tools across physics, probability, statistics …

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 …

Determinantal point processes (DPPs) are an emerging model for encoding probabilities
over subsets, such as shopping baskets, selected from a ground set, such as an item …

Determinantal point processes (DPPs) have attracted substantial attention as an elegant
probabilistic model that captures the balance between quality and diversity within sets …

Direct sampling from a Slater determinant is combined with an autoregressive deep neural
network as a Jastrow factor into a fully autoregressive Slater-Jastrow ansatz for variational …

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 …