10 citations · 19 across the 2 of their papers we have counts for
5 papers · 1 filter
Large deviations for the perceptron model and consequences for active learning
Hugo Cui, Luca Saglietti, Lenka Zdeborová
Active learning is a branch of machine learning that deals with problems where unlabeled data is abundant yet obtaining labels is expensive. The learning algorithm has the possibil…
Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning
Alia Abbara, Benjamin Aubin, Florent Krzakala +1
Statistical learning theory provides bounds of the generalization gap, using in particular the Vapnik-Chervonenkis dimension and the Rademacher complexity. An alternative approach,…
Exact asymptotics for phase retrieval and compressed sensing with random generative priors
Benjamin Aubin, Bruno Loureiro, Antoine Baker +2
We consider the problem of compressed sensing and of (real-valued) phase retrieval with random measurement matrix. We derive sharp asymptotics for the information-theoretically opt…
Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer +5
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent…
Storage capacity in symmetric binary perceptrons
Benjamin Aubin, Will Perkins, Lenka Zdeborová
We study the problem of determining the capacity of the binary perceptron for two variants of the problem where the corresponding constraint is symmetric. We call these variants th…