# Optimistic Online Caching for Batched Requests

arXiv: https://arxiv.org/abs/2310.01309 · HTML: https://mlsanity.com/paper/2310.01309/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [An Online Gradient-Based Caching Policy with Logarithmic Complexity and Regret Guarantees](https://mlsanity.com/paper/2405.01263/) (2024)
- [Efficient and Optimal No-Regret Caching under Partial Observation](https://mlsanity.com/paper/2503.02758/) (2025)
- [Optimistic Learning for Communication Networks](https://mlsanity.com/paper/2504.03499/) (2025)

## Abstract (arXiv)

In this paper we study online caching problems where predictions of future requests, e.g., provided by a machine learning model, are available. Typical online optimistic policies are based on the Follow-The-Regularized-Leader algorithm and have higher computational cost than classic ones like LFU, LRU, as each update of the cache state requires to solve a constrained optimization problem. In this work we analysed the behaviour of two different optimistic policies in a textitbatched case, i.e., when the cache is updated less frequently in order to amortize the update cost over time or over multiple requests. Experimental results show that such an optimistic batched approach outperforms classical caching policies both on stationary and real traces