1 citations · 1 across the 4 of their papers we have counts for
4 papers
Distributionally Robust Optimization with Bias and Variance Reduction
Ronak Mehta, Vincent Roulet, Krishna Pillutla +1
We consider the distributionally robust optimization (DRO) problem with spectral risk-based uncertainty set and -divergence penalty. This formulation includes common risk-sensit…
Dual Gauss-Newton Directions for Deep Learning
Vincent Roulet, Mathieu Blondel
Inspired by Gauss-Newton-like methods, we study the benefit of leveraging the structure of deep learning objectives, namely, the composition of a convex loss function and of a nonl…
Modified Gauss-Newton Algorithms under Noise
Krishna Pillutla, Vincent Roulet, Sham Kakade +1
Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing. Their nonsmooth counterparts, modified Gauss-Newton or prox-linea…
Target Propagation via Regularized Inversion
Vincent Roulet, Zaid Harchaoui
Target Propagation (TP) algorithms compute targets instead of gradients along neural networks and propagate them backward in a way that is similar yet different than gradient back-…