# INFERNO: Inference-Aware Neural Optimisation

arXiv: https://arxiv.org/abs/1806.04743 · HTML: https://mlsanity.com/paper/1806.04743/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 16 of 16)

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- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
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- [Finetuning Foundation Models for Joint Analysis Optimization](https://mlsanity.com/paper/2401.13536/) (2024)
- [A Cautionary Tale of Decorrelating Theory Uncertainties](https://mlsanity.com/paper/2109.08159/) (2021)
- [neos: End-to-End-Optimised Summary Statistics for High Energy Physics](https://mlsanity.com/paper/2203.05570/) (2022)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Bias and Priors in Machine Learning Calibrations for High Energy Physics](https://mlsanity.com/paper/2205.05084/) (2022)
- [FAIR Universe HiggsML Uncertainty Dataset and Competition](https://mlsanity.com/paper/2410.02867/) (2024)
- [Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider](https://mlsanity.com/paper/2105.07530/) (2021)
- [Unknown Unknowns: Model Misspecification in Machine Learning for Physics](https://mlsanity.com/paper/2608.13633/) (2026)
- [Exploring the Universality of Hadronic Jet Classification](https://mlsanity.com/paper/2204.03812/) (2022)
- [Designing Observables for Measurements with Deep Learning](https://mlsanity.com/paper/2310.08717/) (2023)
- [Learning to bin: differentiable and Bayesian optimization for multi-dimensional discriminants in high-energy physics](https://mlsanity.com/paper/2601.07756/) (2026)
- [Uncovering Hidden Systematics in Neural Network Models for High Energy Physics](https://mlsanity.com/paper/2605.07470/) (2026)
- [Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios](https://mlsanity.com/paper/2609.36044/) (2026)

## Abstract (arXiv)

Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data. Furthermore, sometimes one is interested on inference drawn over a subset of the generative model parameters while taking into account model uncertainty or misspecification on the remaining nuisance parameters. In this work, we show how non-linear summary statistics can be constructed by minimising inference-motivated losses via stochastic gradient descent such they provided the smallest uncertainty for the parameters of interest. As a use case, the problem of confidence interval estimation for the mixture coefficient in a multi-dimensional two-component mixture model (i.e. signal vs background) is considered, where the proposed technique clearly outperforms summary statistics based on probabilistic classification, which are a commonly used alternative but do not account for the presence of nuisance parameters.