activity
20172021
most citedDifferentiable Convex Optimization Layers

133 citations · 192 across the 9 of their papers we have counts for

collaborators

25 papers

q-fin.PM20211 cited

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…

stat.ML2021

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…

stat.ML20212 cited

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…

cs.LG2020

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…

q-fin.CP2020

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…

stat.ML2020

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…