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On the Sample Complexity of Learning under Invariance and Geometric Stability
Alberto Bietti, Luca Venturi, Joan Bruna
Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make efficient use of data, it is often useful to leverage certain geom…
Extragradient with player sampling for faster Nash equilibrium finding
Carles Domingo Enrich, Samy Jelassi, Carles Domingo-Enrich +3
Data-driven modeling increasingly requires to find a Nash equilibrium in multi-player games, e.g. when training GANs. In this paper, we analyse a new extra-gradient method for Nash…
Global convergence of neuron birth-death dynamics
Grant Rotskoff, Samy Jelassi, Joan Bruna +1
Neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for the favorable training properties of "o…
Understanding the Learned Iterative Soft Thresholding Algorithm with matrix factorization
Thomas Moreau, Joan Bruna
Sparse coding is a core building block in many data analysis and machine learning pipelines. Typically it is solved by relying on generic optimization techniques, such as the Itera…