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[2602.17699] Certified Learning under Distribution Shift: Sound Verification and Identifiable Structure
Summary
This paper presents a framework for certified learning under distribution shifts, focusing on sound verification and identifiable structures in machine learning models.
Why It Matters
Understanding how machine learning models behave under distribution shifts is crucial for their reliability and safety. This research provides a structured approach to certifying model performance, which can enhance trust in AI systems, particularly in critical applications.
Key Takeaways
- Introduces a unified framework for certifying risk under distribution shifts.
- Establishes explicit upper bounds for excess risk based on computable metrics.
- Emphasizes sound verification of models and interpretability through identifiability.
- Identifies failure modes and non-certifiable regimes in machine learning.
- Addresses the importance of regularity and complexity constraints in model evaluation.
Computer Science > Machine Learning arXiv:2602.17699 (cs) [Submitted on 6 Feb 2026] Title:Certified Learning under Distribution Shift: Sound Verification and Identifiable Structure Authors:Chandrasekhar Gokavarapu, Sudhakar Gadde, Y. Rajasekhar, S. R. Bhargava (Mathematics, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India) View a PDF of the paper titled Certified Learning under Distribution Shift: Sound Verification and Identifiable Structure, by Chandrasekhar Gokavarapu and 7 other authors View PDF HTML (experimental) Abstract: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. Subjects: Machine Learning (cs.LG); Rings and Algebras (math.RA); Machine Learning (stat.ML) MSC classes: 68T05, 62G35, 62G20, 49J20, 90C26 Cite as: arXiv:2602.17699 [cs.LG] (or arXiv:2602.17699v1 [cs.LG] for this version) https://doi.org/10.4...
