18 citations · 56 across the 17 of their papers we have counts for
17 papers
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos +4
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle…
Learning from Multiple Sources for Data-to-Text and Text-to-Data
Song Duong, Alberto Lumbreras, Mike Gartrell +1
Data-to-text (D2T) and text-to-data (T2D) are dual tasks that convert structured data, such as graphs or tables into fluent text, and vice versa. These tasks are usually handled se…
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Elvis Dohmatob +1
A determinantal point process (DPP) is an elegant model that assigns a probability to every subset of a collection of items. While conventionally a DPP is parameterized by a sy…
Scalable Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Jennifer Gillenwater +2
A determinantal point process (DPP) on a collection of items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items.…
Combining Reward and Rank Signals for Slate Recommendation
Imad Aouali, Sergey Ivanov, Mike Gartrell +4
We consider the problem of slate recommendation, where the recommender system presents a user with a collection or slate composed of K recommended items at once. If the user finds…
Wasserstein Learning of Determinantal Point Processes
Lucas Anquetil, Mike Gartrell, Alain Rakotomamonjy +2
Determinantal point processes (DPPs) have received significant attention as an elegant probabilistic model for discrete subset selection. Most prior work on DPP learning focuses on…