# Covariance Prediction via Convex Optimization

arXiv: https://arxiv.org/abs/2101.12416 · HTML: https://mlsanity.com/paper/2101.12416/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 57 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Structured Uncertainty Prediction Networks](https://mlsanity.com/paper/1802.07079/) (2018)
- [A Distributed Method for Fitting Laplacian Regularized Stratified Models](https://mlsanity.com/paper/1904.12017/) (2019)
- [Fitting Laplacian Regularized Stratified Gaussian Models](https://mlsanity.com/paper/2005.01752/) (2020)
- [Portfolio Construction Using Stratified Models](https://mlsanity.com/paper/2101.04113/) (2021)

## Built on by (most cited first, 4 of 4)

- [End-to-end Conditional Robust Optimization](https://mlsanity.com/paper/2403.04670/) (2024)
- [Fitting Multilevel Factor Models](https://mlsanity.com/paper/2409.12067/) (2024)
- [Entropic covariance models](https://mlsanity.com/paper/2306.03590/) (2023)
- [Self-Supervised Learning for Covariance Estimation](https://mlsanity.com/paper/2403.08662/) (2024)

## Abstract (arXiv)

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 generalized linear model, i.e., an affine function of the features followed by an inverse link function that maps vectors to symmetric positive definite matrices. The log-likelihood is a concave function of the predictor parameters, so fitting the predictor involves convex optimization. Such predictors can be combined with others, or recursively applied to improve performance.