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8 papers
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…
Approximating Lipschitz continuous functions with GroupSort neural networks
Ugo Tanielian, Maxime Sangnier, Gerard Biau
Recent advances in adversarial attacks and Wasserstein GANs have advocated for use of neural networks with restricted Lipschitz constants. Motivated by these observations, we study…
Learning disconnected manifolds: a no GANs land
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob +1
Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator. Consequently, the modeled distribution alw…
Relaxed Softmax for learning from Positive and Unlabeled data
Ugo Tanielian, Flavian Vasile
In recent years, the softmax model and its fast approximations have become the de-facto loss functions for deep neural networks when dealing with multi-class prediction. This loss…
Distributionally Robust Counterfactual Risk Minimization
Louis Faury, Ugo Tanielian, Flavian Vasile +2
This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem. Tapping into a rich existing literat…
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…