Universal Caching
Ativ Joshi, Abhishek Sinha

TL;DR
This paper introduces a universal online caching policy that minimizes regret against a flexible, finite-state benchmark, achieving the first data-dependent regret bounds in this setting using information-theoretic methods.
Contribution
It proposes an efficient universal caching algorithm with sub-linear regret bounds, extending the analysis to finite-state benchmarks and applying information theory techniques.
Findings
First data-dependent regret bounds for universal caching
Achieves sub-linear regret in non-stationary settings
Combines online caching with Lempel-Ziv parsing for improved analysis
Abstract
In learning theory, the performance of an online policy is commonly measured in terms of the static regret metric, which compares the cumulative loss of an online policy to that of an optimal benchmark in hindsight. In the definition of static regret, the action of the benchmark policy remains fixed throughout the time horizon. Naturally, the resulting regret bounds become loose in non-stationary settings where fixed actions often suffer from poor performance. In this paper, we investigate a stronger notion of regret minimization in the context of online caching. In particular, we allow the action of the benchmark at any round to be decided by a finite state machine containing any number of states. Popular caching policies, such as LRU and FIFO, belong to this class. Using ideas from the universal prediction literature in information theory, we propose an efficient online caching policy…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Machine Learning and Algorithms · Optimization and Search Problems
