Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Gartrell, Mike"'
Autor:
Sebag, Ilana, Pydi, Muni Sreenivas, Franceschi, Jean-Yves, Rakotomamonjy, Alain, Gartrell, Mike, Atif, Jamal, Allauzen, Alexandre
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling. This can be achieved through either differentially private stochastic gradient descent or a differentially private metric for training m
Externí odkaz:
http://arxiv.org/abs/2312.08227
Autor:
Franceschi, Jean-Yves, Gartrell, Mike, Santos, Ludovic Dos, Issenhuth, Thibaut, de Bézenac, Emmanuel, Chen, Mickaël, Rakotomamonjy, Alain
Publikováno v:
Thirty-seventh Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, Dec. 2023, New Orleans, LA, USA
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 of displacing particle distributions using differential equations is c
Externí odkaz:
http://arxiv.org/abs/2305.16150
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 separately and use corpora extracted from a single source. Current syste
Externí odkaz:
http://arxiv.org/abs/2302.11269
A determinantal point process (DPP) is an elegant model that assigns a probability to every subset of a collection of $n$ items. While conventionally a DPP is parameterized by a symmetric kernel matrix, removing this symmetry constraint, resulting in
Externí odkaz:
http://arxiv.org/abs/2207.00486
A determinantal point process (DPP) on a collection of $M$ items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items. Recent work shows that removing the kernel symmetry constraint, yieldi
Externí odkaz:
http://arxiv.org/abs/2201.08417
Autor:
Aouali, Imad, Ivanov, Sergey, Gartrell, Mike, Rohde, David, Vasile, Flavian, Zaytsev, Victor, Legrand, Diego
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 the recommended items appealing then the user may click and the recomm
Externí odkaz:
http://arxiv.org/abs/2107.12455
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 maximum likelihood estimation (MLE). While efficient and scalable, ML
Externí odkaz:
http://arxiv.org/abs/2011.09712
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 shows that nonsymmetric DPP (NDPP) kernels have significant advantages ov
Externí odkaz:
http://arxiv.org/abs/2006.09862
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, contextual constraints need to be applied to refine recommendations,
Externí odkaz:
http://arxiv.org/abs/1907.01637
Determinantal point processes (DPPs) have attracted substantial attention as an elegant probabilistic model that captures the balance between quality and diversity within sets. DPPs are conventionally parameterized by a positive semi-definite kernel
Externí odkaz:
http://arxiv.org/abs/1905.12962