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[2602.21390] Defensive Generation
Summary
The paper 'Defensive Generation' presents a novel approach to creating generative models that are unfalsifiable based on observed data, enhancing online high-dimensional multicalibration techniques.
Why It Matters
This research addresses critical challenges in machine learning related to the reliability and robustness of generative models. By ensuring that these models cannot be falsified through extensive testing, it opens avenues for safer AI applications, particularly in sensitive areas requiring high trust.
Key Takeaways
- Introduces 'Defensive Generation' for creating unfalsifiable generative models.
- Enhances online high-dimensional multicalibration techniques.
- Achieves optimal generation error rates in near-linear time.
- Addresses the challenge of outcome indistinguishability in AI models.
- Contributes to safer AI practices by ensuring model reliability.
Computer Science > Machine Learning arXiv:2602.21390 (cs) [Submitted on 24 Feb 2026] Title:Defensive Generation Authors:Gabriele Farina, Juan Carlos Perdomo View a PDF of the paper titled Defensive Generation, by Gabriele Farina and Juan Carlos Perdomo View PDF HTML (experimental) Abstract:We study the problem of efficiently producing, in an online fashion, generative models of scalar, multiclass, and vector-valued outcomes that cannot be falsified on the basis of the observed data and a pre-specified collection of computational tests. Our contributions are twofold. First, we expand on connections between online high-dimensional multicalibration with respect to an RKHS and recent advances in expected variational inequality problems, enabling efficient algorithms for the former. We then apply this algorithmic machinery to the problem of outcome indistinguishability. Our procedure, Defensive Generation, is the first to efficiently produce online outcome indistinguishable generative models of non-Bernoulli outcomes that are unfalsifiable with respect to infinite classes of tests, including those that examine higher-order moments of the generated distributions. Furthermore, our method runs in near-linear time in the number of samples and achieves the optimal, vanishing T^{-1/2} rate for generation error. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2602.21390 [cs.LG] (or arXiv:2602.21390v1 [cs.LG] for this version) https://doi.org/10.48550/...
