7 citations · 12 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…