aiqbench analysis
This paper advances robust conformal prediction by leveraging half-mass radius and nearest-neighbor distances to maintain finite-sample validity under data contamination. The authors establish rigorous theoretical guarantees, including consistency in symmetric-difference probability and quantitative stability against arbitrary small-mass perturbations. These contributions strengthen the statistical foundations required for reliable uncertainty quantification in adversarial or noisy machine learning environments.
Conformal Robust Set Estimation
arXiv:2604.18441v2 Announce Type: replace-cross Abstract: We introduce a robust conformal prediction method based on the half-mass radius $\dP(x):=\inf\{r>0:P(B(x,r))>1/2\}$. Its empirical version is exactly the distance from a point to its $\mathbf{k}$-th nearest neighbour, with $\mathbf{k}=\lfloor n/2\rfloor+1$. The resulting conformal sets retain finite-sample marginal validity and asymptotically target the robust population central set $\Qba:=\{x\in\R^d:\dP(x)\le\beta_\alpha\}$. We prove consistency in symmetric-difference probability, derive non-asymptotic exponential bounds and obtain the rate $O((\log n/n)^{1/2})$ under a local mass condition. Under the same local regularity on the uncontaminated law, we also establish quantitative $O(\eta)$ stability, uniformly over arbitrary contaminations of sufficiently small mass $\eta$, prove Hausdorff convergence of the empirical central sets, and provide computable ball-based representations using only pairwise distances.
Abstract:We introduce a robust conformal prediction method based on the half-mass radius $\dP(x):=\inf\{r>0:P(B(x,r))>1/2\}$. Its empirical version is exactly the distance from a point to its $\mathbf{k}$-th nearest neighbour, with $\mathbf{k}=\lfloor n/2\rfloor+1$. The resulting conformal sets retain finite-sample marginal validity and asymptotically target the robust population central set $\Qba:=\{x\in\R^d:\dP(x)\le\beta_\alpha\}$. We prove consistency in symmetric-difference probability, derive non-asymptotic exponential bounds and obtain the rate $O((\log n/n)^{1/2})$ under a local mass condition. Under the same local regularity on the uncontaminated law, we also establish quantitative $O(\eta)$ stability, uniformly over arbitrary contaminations of sufficiently small mass $\eta$, prove Hausdorff convergence of the empirical central sets, and provide computable ball-based representations using only pairwise distances.
Statistics Theory (math.ST); Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2604.18441 [math.ST]
(or arXiv:2604.18441v2 [math.ST] for this version)
https://doi.org/10.48550/arXiv.2604.18441 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 208 words
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