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

In this paper we present a Bayesian method for learning a low-rank factorization of this kernel, which provides automatic control of regularization. We show.

Aug 16, 2016 · Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, ...

Aug 15, 2016 · In this paper we present a low-rank DPP mixture model that allows us to represent the latent structure present in observed subsets as a mixture ...

In this paper we present a Bayesian method for learning a low-rank factorization of this kernel, which provides automatic control of regularization. We show.

This paper presents an efficient and scalable Markov Chain Monte Carlo (MCMC) learning algorithm for the model that uses Gibbs sampling and stochastic ...

Duration: 20:11
Posted: Mar 30, 2017

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

3 A new regularized determinantal point process. In this section we develop the theory for a novel regular- ized extension of determinantal point processes (DPP).

Julia implementation of low-rank determinantal point process (DPP) learning and prediction algorithms. Two learning algorithms are provided: the first is an ...