TL;DR
This paper explores methods for transforming offline estimators into online predictors with minimal additional regret, analyzing their advantages, limitations, and computational complexity, and providing a new derivation of Turing's estimator.
Contribution
It introduces approaches for converting offline estimators to online, analyzes their trade-offs, and determines the computational complexity of effective online estimators.
Findings
Various approaches have different pros and cons.
The computational complexity of online estimators with guarantees is characterized.
A new combinatoric derivation of Turing's estimator is provided.
Abstract
We consider the problem of converting offline estimators into an online predictor or estimator with small extra regret. Formally this is the problem of merging a collection of probability measures over strings of length 1,2,3,... into a single probability measure over infinite sequences. We describe various approaches and their pros and cons on various examples. As a side-result we give an elementary non-heuristic purely combinatoric derivation of Turing's famous estimator. Our main technical contribution is to determine the computational complexity of online estimators with good guarantees in general.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputability, Logic, AI Algorithms · Algorithms and Data Compression · semigroups and automata theory