133 citations · 192 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
InterpNET: Neural Introspection for Interpretable Deep Learning
Shane Barratt
Humans are able to explain their reasoning. On the contrary, deep neural networks are not. This paper attempts to bridge this gap by introducing a new way to design interpretable n…