Stabilizing black-box algorithms through task-oriented randomization
As black-box models become foundational to modern research, ensuring their stability is paramount for the realization of trustworthy artificial intelligence. The inherent diversity of inputs - ranging from structured Gaussian distributions to complex data with unknown structures - poses a significant challenge: how to stabilize black-box outputs while effectively leveraging available prior information. This paper introduces a task-oriented randomization methodology that adaptively tailors its strategy to the underlying generative mechanisms of the input data, specifically addressing unstructured complexities. A comprehensive suite of stability guarantees is proposed. Beyond establishing r...
| Authors | Yali Wang, Zhaojun Wang |
|---|---|
| Primary category | stat.ML |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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