Bounds on training error come from observed cycle lengths
A bound uses observed cycle lengths to adjust training on time-based data without knowing all dependencies.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Imon Banerjee, Pierre Alquier
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Researchers built a bound on training error that uses how long data cycles last. It has a main part and a tail term; the tail is usually small but not always negligible.
Why it matters
For teams training models on streams or time-based data, this helps set realistic error expectations without guessing hidden links.
Treat data as blocks between regeneration points. Use the actual cycle lengths to estimate how far your training score might be off.
The second-order tail term is usually small, but its size depends on how long cycles can be.
What remains uncertain
The tail term can be meaningful if regeneration times are long or highly variable; bounds depend on observed cycles.
Original sources · 1
- Self-normalized Bernstein Inequalities for Regenerative Processes and Applications to Learning Without Independence ↗arXiv · 2026-10-21
Check the original paper for its authors, methods, version and access terms.
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