233 citations · 531 across the 34 of their papers we have counts for
Showing 2019 · stat.MLShow all
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stat.ML2019
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks
Otmane Sakhi, Stephen Bonner, David Rohde +1
The combination of the re-parameterization trick with the use of variational auto-encoders has caused a sensation in Bayesian deep learning, allowing the training of realistic gene…
stat.ML2019★ 7 cited
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…
stat.ML2019
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…