Publications (20)
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…
Learning Determinantal Point Processes by Corrective Negative Sampling
Zelda Mariet, Mike Gartrell, Suvrit Sra
Determinantal Point Processes (DPPs) have attracted significant interest from the machine-learning community due to their ability to elegantly and tractably model the delicate bala…
Adversarial Training of Word2Vec for Basket Completion
Ugo Tanielian, Mike Gartrell, Flavian Vasile
In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language mo…
Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
Eduardo Fernandes Montesuma, Yassir Bendou, Mike Gartrell
Wasserstein barycenters provide a principled approach for aggregating probability measures, while preserving the geometry of their ambient space. Existing discrete methods are not…
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…
The Bayesian Low-Rank Determinantal Point Process Mixture Model
Mike Gartrell, Ulrich Paquet, Noam Koenigstein
Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog. They are usef…
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…
Low-Rank Factorization of Determinantal Point Processes for Recommendation
Mike Gartrell, Ulrich Paquet, Noam Koenigstein
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 p…
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.…
Embedding models for recommendation under contextual constraints
Syrine Krichene, Mike Gartrell, Clement Calauzenes
Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications…
Deep Determinantal Point Processes
Mike Gartrell, Elvis Dohmatob, Jon Alberdi
Determinantal point processes (DPPs) have attracted significant attention as an elegant model that is able to capture the balance between quality and diversity within sets. DPPs ar…
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Mike Gartrell, Insu Han, Elvis Dohmatob +2
Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work sho…
Multi-Task Determinantal Point Processes for Recommendation
Romain Warlop, Jérémie Mary, Mike Gartrell
Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to el…
GEVR: An Event Venue Recommendation System for Groups of Mobile Users
Jason Shuo Zhang, Mike Gartrell, Richard Han +2
In this paper, we present GEVR, the first Group Event Venue Recommendation system that incorporates mobility via individual location traces and context information into a "social-b…
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…
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi +4
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling. This can be achieved through either differentially private stochas…
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…
Learning Nonsymmetric Determinantal Point Processes
Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob +1
Determinantal point processes (DPPs) have attracted substantial attention as an elegant probabilistic model that captures the balance between quality and diversity within sets. DPP…
ReBaPL: Repulsive Bayesian Prompt Learning
Yassir Bendou, Omar Ezzahir, Eduardo Fernandes Montesuma +3
Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning methods are prone to…
Understanding Group Event Scheduling via the OutWithFriendz Mobile Application
Shuo Zhang, Khaled Alanezi, Mike Gartrell +3
The wide adoption of smartphones and mobile applications has brought significant changes to not only how individuals behave in the real world, but also how groups of users interact…