12 citations · 28 across the 14 of their papers we have counts for
17 papers
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…
Stochastic Optimization for Spectral Risk Measures
Ronak Mehta, Vincent Roulet, Krishna Pillutla +2
Spectral risk objectives - also called -risks - allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and wor…
Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel +1
Iterative optimization algorithms depend on access to information about the objective function. In a differentiable programming framework, this information, such as gradients, can…
On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel +1
A classical approach for solving discrete time nonlinear control on a finite horizon consists in repeatedly minimizing linear quadratic approximations of the original problem aroun…
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-…