# Risk-Averse Decision Making with Multi-Level Reliability Guarantees

arXiv: https://arxiv.org/abs/2609.11524 · HTML: https://mlsanity.com/paper/2609.11524/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents](https://mlsanity.com/paper/2502.02561/) (2025)
- [Nested Conformal Prediction Sets for Classification with Applications to Probation Data](https://mlsanity.com/paper/2104.09358/) (2021)
- [Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems](https://mlsanity.com/paper/2504.09310/) (2025)
- [Calibrated Multi-Level Quantile Forecasting](https://mlsanity.com/paper/2512.23671/) (2025)
- [Online Conformal Prediction: Enforcing monotonicity via Online Optimization](https://mlsanity.com/paper/2605.12668/) (2026)

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

- [Conformal Sequential Decision Making With Cost and Sublinear Policy Regret Guarantees](https://mlsanity.com/paper/2610.15011/) (2026)

## Abstract (arXiv)

Many applications in engineering, including wireless broadcasting, require designs that provide performance certificates at different target outage levels. This paper studies the problem of maximizing the weighted average of such certificates in the presence of uncertainty about the true system state. The problem is shown to be equivalent to an optimization over nested prediction sets, connecting to the literature on conformal prediction and extending prior art on single-level risk-averse decision making. Furthermore, we derive a dual formulation that decouples optimization across input values. Numerical experiments on a diversity-based wireless transmission system illustrate the cost of enforcing multi-level certificates with a single shared policy and trace the Pareto trade-off between multiple reliability levels.