221 citations · 588 across the 17 of their papers we have counts for
5 papers · 1 filter
Learning Convex Optimization Models
Akshay Agrawal, Shane Barratt, Stephen Boyd
A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cas…
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…
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…
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data
Shane Barratt, Mykel Kochenderfer, Stephen Boyd
Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offl…
Improved Training with Curriculum GANs
Rishi Sharma, Shane Barratt, Stefano Ermon +1
In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the co…