221 citations · 588 across the 17 of their papers we have counts for
7 papers · 1 filter
Learning Convex Optimization Control Policies
Akshay Agrawal, Shane Barratt, Stephen Boyd +1
Many control policies used in various applications determine the input or action by solving a convex optimization problem that depends on the current state and some parameters. Com…
Minimizing a Sum of Clipped Convex Functions
Shane Barratt, Guillermo Angeris, Stephen Boyd
We consider the problem of minimizing a sum of clipped convex functions; applications include clipped empirical risk minimization and clipped control. While the problem of minimizi…
Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos, Shane Barratt +3
Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectur…
Fitting a Kalman Smoother to Data
Shane Barratt, Stephen Boyd
This paper considers the problem of fitting the parameters of a Kalman smoother to data. We formulate the Kalman smoothing problem with missing measurements as a constrained least…
Least Squares Auto-Tuning
Shane Barratt, Stephen Boyd
Least squares is by far the simplest and most commonly applied computational method in many fields. In almost all applications, the least squares objective is rarely the true objec…
A Distributed Method for Fitting Laplacian Regularized Stratified Models
Jonathan Tuck, Shane Barratt, Stephen Boyd
Stratified models are models that depend in an arbitrary way on a set of selected categorical features, and depend linearly on the other features. In a basic and traditional formul…
Differentiating Through a Cone Program
Akshay Agrawal, Shane Barratt, Stephen Boyd +2
We consider the problem of efficiently computing the derivative of the solution map of a convex cone program, when it exists. We do this by implicitly differentiating the residual…