Search Results for author: Shane Barratt

Found 20 papers, 15 papers with code

Covariance Prediction via Convex Optimization

1 code implementation29 Jan 2021 Shane Barratt, Stephen Boyd

We consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector.

Low Rank Forecasting

no code implementations29 Jan 2021 Shane Barratt, Yining Dong, Stephen Boyd

Our focus is on low rank forecasters, which break forecasting up into two steps: estimating a vector that can be interpreted as a latent state, given the past, and then estimating the future values of the time series, given the latent state estimate.

Time Series Time Series Analysis

Portfolio Construction Using Stratified Models

no code implementations11 Jan 2021 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 these conditions, and the current portfolio holdings.

Learning Convex Optimization Models

1 code implementation7 Jun 2020 Akshay Agrawal, Shane Barratt, Stephen Boyd

A convex optimization model predicts an output from an input by solving a convex optimization problem.


Optimal Representative Sample Weighting

1 code implementation18 May 2020 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 of the data are close to prescribed values.

Multi-Period Liability Clearing via Convex Optimal Control

1 code implementation18 May 2020 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.

Convex Optimization Over Risk-Neutral Probabilities

1 code implementation5 Mar 2020 Shane Barratt, Jonathan Tuck, Stephen Boyd

We describe a number of convex optimization problems over the convex set of risk neutral price probabilities.

Automatic Repair of Convex Optimization Problems

1 code implementation29 Jan 2020 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 that the problem becomes solvable?

Optimization and Control

Learning Convex Optimization Control Policies

no code implementations L4DC 2020 Akshay Agrawal, Shane Barratt, Stephen Boyd, Bartolomeo Stellato

Common examples of such convex optimization control policies (COCPs) include the linear quadratic regulator (LQR), convex model predictive control (MPC), and convex control-Lyapunov or approximate dynamic programming (ADP) policies.

Model Predictive Control

Differentiable Convex Optimization Layers

1 code implementation NeurIPS 2019 Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, Zico Kolter

In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization.

Inductive Bias

Minimizing a Sum of Clipped Convex Functions

1 code implementation27 Oct 2019 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.

A Distributed Method for Fitting Laplacian Regularized Stratified Models

2 code implementations26 Apr 2019 Jonathan Tuck, Shane Barratt, Stephen Boyd

In a basic and traditional formulation a separate model is fit for each value of the categorical feature, using only the data that has the specific categorical value.

Differentiating Through a Cone Program

1 code implementation19 Apr 2019 Akshay Agrawal, Shane Barratt, Stephen Boyd, Enzo Busseti, Walaa M. Moursi

These correspond to computing an approximate new solution, given a perturbation to the cone program coefficients (i. e., perturbation analysis), and to computing the gradient of a function of the solution with respect to the coefficients.

Optimization and Control

Least Squares Auto-Tuning

1 code implementation10 Apr 2019 Shane Barratt, Stephen Boyd

Least squares is by far the simplest and most commonly applied computational method in many fields.

Improved Training with Curriculum GANs

no code implementations24 Jul 2018 Rishi Sharma, Shane Barratt, Stefano Ermon, Vijay Pande

We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation.

Image Generation

Optimizing for Generalization in Machine Learning with Cross-Validation Gradients

1 code implementation18 May 2018 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 performance.

BIG-bench Machine Learning Hyperparameter Optimization +1

A Note on the Inception Score

8 code implementations6 Jan 2018 Shane Barratt, Rishi Sharma

Deep generative models are powerful tools that have produced impressive results in recent years.

InterpNET: Neural Introspection for Interpretable Deep Learning

1 code implementation26 Oct 2017 Shane Barratt

This paper proposes a neural network design paradigm, termed InterpNET, which can be combined with any existing classification architecture to generate natural language explanations of the classifications.

Classification General Classification +1

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