221 citations · 588 across the 17 of their papers we have counts for
26 papers
Portfolio Construction Using Stratified Models
Jonathan Tuck, Shane Barratt, Stephen Boyd
In this paper we develop models of asset return mean and covariance that depend on some observable market conditions, and use these to construct a trading policy that depends on th…
Covariance Prediction via Convex Optimization
Shane Barratt, Stephen Boyd
We consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector. We describe a covariance predictor that has the form of a gene…
Low Rank Forecasting
Shane Barratt, Yining Dong, Stephen Boyd
We consider the problem of forecasting multiple values of the future of a vector time series, using some past values. This problem, and related ones such as one-step-ahead predicti…
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…
Multi-Period Liability Clearing via Convex Optimal Control
Shane Barratt, Stephen Boyd
We consider the problem of determining a sequence of payments among a set of entities that clear (if possible) the liabilities among them. We formulate this as an optimal control p…
Optimal Representative Sample Weighting
Shane Barratt, Guillermo Angeris, Stephen Boyd
We consider the problem of assigning weights to a set of samples or data records, with the goal of achieving a representative weighting, which happens when certain sample averages…
Convex Optimization Over Risk-Neutral Probabilities
Shane Barratt, Jonathan Tuck, Stephen Boyd
We consider a collection of derivatives that depend on the price of an underlying asset at expiration or maturity. The absence of arbitrage is equivalent to the existence of a risk…
Automatic Repair of Convex Optimization Problems
Shane Barratt, Guillermo Angeris, Stephen Boyd
Given an infeasible, unbounded, or pathological convex optimization problem, a natural question to ask is: what is the smallest change we can make to the problem's parameters such…
Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint
Malayandi Palan, Shane Barratt, Alex McCauley +3
We consider the problem of learning a linear control policy for a linear dynamical system, from demonstrations of an expert regulating the system. The standard approach to this pro…
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…
Stochastic Control with Affine Dynamics and Extended Quadratic Costs
Shane Barratt, Stephen Boyd
An extended quadratic function is a quadratic function plus the indicator function of an affine set, that is, a quadratic function with embedded linear equality constraints. We sho…
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…
Optimizing for Generalization in Machine Learning with Cross-Validation Gradients
Shane Barratt, Rishi Sharma
Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization per…
A Matrix Gaussian Distribution
Shane Barratt
In this note, we define a Gaussian probability distribution over matrices. We prove some useful properties of this distribution, namely, the fact that marginalization, conditioning…
On the Differentiability of the Solution to Convex Optimization Problems
Shane Barratt
In this paper, we provide conditions under which one can take derivatives of the solution to convex optimization problems with respect to problem data. These conditions are (roughl…
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication
Colin de Vrieze, Shane Barratt, Daniel Tsai +1
Traditional radio systems are strictly co-designed on the lower levels of the OSI stack for compatibility and efficiency. Although this has enabled the success of radio communicati…
A Note on the Inception Score
Shane Barratt, Rishi Sharma
Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential…