# Efficient Black-Box Importance Sampling for VaR and CVaR Estimation

arXiv: https://arxiv.org/abs/2106.10236 · HTML: https://mlsanity.com/paper/2106.10236/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 16 Jun 2021.

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

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers](https://mlsanity.com/paper/2102.07060/) (2021)
- [Rare-Event Simulation for Neural Network and Random Forest Predictors](https://mlsanity.com/paper/2010.04890/) (2020)
- [Optimizing tail risks using an importance sampling based extrapolation for heavy-tailed objectives](https://mlsanity.com/paper/2008.09818/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty](https://mlsanity.com/paper/2411.02654/) (2024)

## Abstract (arXiv)

This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined in terms of a sophisticated object such as a machine learning feature map or a mixed integer linear optimisation formulation. Assuming only black-box access to the loss and the distribution of the underlying random vector, the paper presents an efficient IS algorithm for estimating the Value at Risk and Conditional Value at Risk. The key challenge in any IS procedure, namely, identifying an appropriate change-of-measure, is automated with a self-structuring IS transformation that learns and replicates the concentration properties of the conditional excess from less rare samples. The resulting estimators enjoy asymptotically optimal variance reduction when viewed in the logarithmic scale. Simulation experiments highlight the efficacy and practicality of the proposed scheme