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09/08/2023
AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting
In digital advertising, the selection of the optimal item (recommendatio...
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07/21/2022
Optimal precision for GANs
When learning disconnected distributions, Generative adversarial network...
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01/31/2022
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge
Designing data sharing mechanisms providing performance and strong priva...
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01/08/2022
Optimal 1-Wasserstein Distance for WGANs
The mathematical forces at work behind Generative Adversarial Networks r...
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11/30/2021
EdiBERT, a generative model for image editing
Advances in computer vision are pushing the limits of im-age manipulatio...
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10/19/2021
Latent reweighting, an almost free improvement for GANs
Standard formulations of GANs, where a continuous function deforms a con...
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09/02/2021
What Users Want? WARHOL: A Generative Model for Recommendation
Current recommendation approaches help online merchants predict, for eac...
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11/19/2020
Wasserstein Learning of Determinantal Point Processes
Determinantal point processes (DPPs) have received significant attention...
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06/09/2020
Approximating Lipschitz continuous functions with GroupSort neural networks
Recent advances in adversarial attacks and Wasserstein GANs have advocat...
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06/08/2020
Learning disconnected manifolds: a no GANs land
Typical architectures of Generative AdversarialNetworks make use of a un...
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06/04/2020
Some Theoretical Insights into Wasserstein GANs
Generative Adversarial Networks (GANs) have been successful in producing...
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09/17/2019
Relaxed Softmax for learning from Positive and Unlabeled data
In recent years, the softmax model and its fast approximations have beco...
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06/14/2019
Distributionally Robust Counterfactual Risk Minimization
This manuscript introduces the idea of using Distributionally Robust Opt...
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05/22/2018
Adversarial Training of Word2Vec for Basket Completion
In recent years, the Word2Vec model trained with the Negative Sampling l...
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03/28/2018