Certified Learning under Distribution Shift: Sound Verification and Identifiable Structure
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915808629751808 |
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| author | Gokavarapu, Chandrasekhar Gadde, Sudhakar Rajasekhar, Y. Bhargava, S. R. |
| author_facet | Gokavarapu, Chandrasekhar Gadde, Sudhakar Rajasekhar, Y. Bhargava, S. R. |
| contents | Proposition. Let $f$ be a predictor trained on a distribution $P$ and evaluated on a shifted distribution $Q$. Under verifiable regularity and complexity constraints, the excess risk under shift admits an explicit upper bound determined by a computable shift metric and model parameters. We develop a unified framework in which (i) risk under distribution shift is certified by explicit inequalities, (ii) verification of learned models is sound for nontrivial sizes, and (iii) interpretability is enforced through identifiability conditions rather than post hoc explanations. All claims are stated with explicit assumptions. Failure modes are isolated. Non-certifiable regimes are characterized. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_17699 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Certified Learning under Distribution Shift: Sound Verification and Identifiable Structure Gokavarapu, Chandrasekhar Gadde, Sudhakar Rajasekhar, Y. Bhargava, S. R. Machine Learning Rings and Algebras 68T05, 62G35, 62G20, 49J20, 90C26 Proposition. Let $f$ be a predictor trained on a distribution $P$ and evaluated on a shifted distribution $Q$. Under verifiable regularity and complexity constraints, the excess risk under shift admits an explicit upper bound determined by a computable shift metric and model parameters. We develop a unified framework in which (i) risk under distribution shift is certified by explicit inequalities, (ii) verification of learned models is sound for nontrivial sizes, and (iii) interpretability is enforced through identifiability conditions rather than post hoc explanations. All claims are stated with explicit assumptions. Failure modes are isolated. Non-certifiable regimes are characterized. |
| title | Certified Learning under Distribution Shift: Sound Verification and Identifiable Structure |
| topic | Machine Learning Rings and Algebras 68T05, 62G35, 62G20, 49J20, 90C26 |
| url | https://arxiv.org/abs/2602.17699 |