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Inductive Venn-Abers and related regressors
- Authors
- Ivan Petej, Vladimir Vovk
Brief
The paper extends Venn–Abers probabilistic predictors—previously limited to binary classification and recently to bounded regression—to unbounded regression by incorporating conformal-prediction machinery. The authors construct point regressors from these probabilistic outputs and present simulation and empirical experiments that indicate modest gains in predictive efficiency for larger training sets. Full paper (33 pages) is available on arXiv.
Why it matters
Ivan Petej and Vladimir Vovk (arXiv 2026-05-07, 33 pages) generalize Venn–Abers predictors from binary classification and recent bounded-regression extensions to unbounded regression by integrating an element of conformal prediction.
Key details
- They derive point regressors from the new Venn–Abers regressors and report simulation and empirical studies showing modest improvements in predictive efficiency for larger training-set sizes.
- Paper available as arXiv:2605.06646v1 (PDF https://arxiv.org/pdf/2605.06646v1).