Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 67 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 30 tok/s Pro
GPT-5 High 29 tok/s Pro
GPT-4o 128 tok/s Pro
Kimi K2 204 tok/s Pro
GPT OSS 120B 461 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Generic Bounds on the Maximum Deviations in Sequential Prediction: An Information-Theoretic Analysis (1910.06742v3)

Published 11 Oct 2019 in cs.LG, cs.IT, eess.SP, math.IT, math.ST, stat.ML, and stat.TH

Abstract: In this paper, we derive generic bounds on the maximum deviations in prediction errors for sequential prediction via an information-theoretic approach. The fundamental bounds are shown to depend only on the conditional entropy of the data point to be predicted given the previous data points. In the asymptotic case, the bounds are achieved if and only if the prediction error is white and uniformly distributed.

Citations (6)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (2)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.