98 citations · 154 across the 15 of their papers we have counts for
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PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli +2
Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance…
Convergence Analysis of Homotopy-SGD for non-convex optimization
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh +2
First-order stochastic methods for solving large-scale non-convex optimization problems are widely used in many big-data applications, e.g. training deep neural networks as well as…
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs
Matilde Gargiani, Andrea Zanelli, Moritz Diehl +1
Following early work on Hessian-free methods for deep learning, we study a stochastic generalized Gauss-Newton method (SGN) for training DNNs. SGN is a second-order optimization me…