A general recipe for data-adaptive anytime-valid envelopes for iterative algorithms
Most convergence guarantees for iterative stochastic algorithms are stated at a fixed iteration count and do not justify stopping at a data-dependent time. Recent work has constructed anytime-valid envelopes that do, but deterministic envelopes are typically too conservative for practical stopping, and existing data-adaptive ones are derived case-by-case, each coupling a specific algorithm to a specific noise model and boundary construction. This paper provides a general recipe for constructing such anytime-valid data-adaptive boundaries for a large class of iterative algorithms. Performance measures of many of these algorithms satisfy a one-step recursion that reduces the entire error an...
| Authors | Soham Bonnerjee, Aaditya Ramdas |
|---|---|
| Primary category | stat.ML |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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