# Inductive randomness predictors: beyond conformal

arXiv: https://arxiv.org/abs/2503.02803 · HTML: https://mlsanity.com/paper/2503.02803/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [Learning by Transduction](https://mlsanity.com/paper/1301.7375/) (2013)
- [Conformal e-prediction](https://mlsanity.com/paper/2001.05989/) (2020)
- [Universality of conformal prediction under the assumption of randomness](https://mlsanity.com/paper/2502.19254/) (2025)
- [An error analysis for polynomial optimization over the simplex based on the multivariate hypergeometric distribution](https://mlsanity.com/paper/1407.2108/) (2014)
- [On the convergence rate of grid search for polynomial optimization over the simplex](https://mlsanity.com/paper/1510.01163/) (2015)
- [Test statistics and p-values](https://mlsanity.com/paper/1702.02590/) (2017)

## Abstract (arXiv)

This paper introduces inductive randomness predictors, which form a proper superset of inductive conformal predictors but have the same principal property of validity under the assumption of randomness (i.e., of IID data). It turns out that every non-trivial inductive conformal predictor is strictly dominated by an inductive randomness predictor, although the improvement is not great, at most a factor of e≈2.72 in the case of e-prediction. The dominating inductive randomness predictors are more complicated and more difficult to compute; besides, an improvement by a factor of e is rare. Therefore, this paper does not suggest replacing inductive conformal predictors by inductive randomness predictors and only calls for a more detailed study of the latter.