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5 papers · 1 filter
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation
Maxime Haddouche, Benjamin Guedj
PAC-Bayes learning is an established framework to both assess the generalisation ability of learning algorithms, and design new learning algorithm by exploiting generalisation boun…
Measuring dissimilarity with diffeomorphism invariance
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj +1
Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms. We introduce DID, a pairwise dissimilarity measure applicable to a wide range of…
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Alexander Camuto, George Deligiannidis, Murat A. Erdogdu +3
Understanding generalization in deep learning has been one of the major challenges in statistical learning theory over the last decade. While recent work has illustrated that the d…
Private Protocols for U-Statistics in the Local Model and Beyond
James Bell, Aurélien Bellet, Adrià Gascón +1
In this paper, we study the problem of computing -statistics of degree , i.e., quantities that come in the form of averages over pairs of data points, in the local model of d…
Grammar Variational Autoencoder
Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video and audio. However, generative modeling of discrete…