Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Algorithms for Caching and MTS with reduced number of predictions
Authors: Karim Ahmed Abdel Sadek, Marek Elias
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform an empirical evaluation of our caching algorithm F&R on the same datasets and with the same predictors as the previous works (Lykouris and Vassilvitskii, 2021; Antoniadis et al., 2023; Im et al., 2022). |
| Researcher Affiliation | Academia | Karim Abdel Sadek University of Amsterdam EMAIL Marek Eliáš Department of Computing Sciences Bocconi University EMAIL |
| Pseudocode | Yes | Algorithm 1: Follower; Algorithm 2: Robustf (one phase) |
| Open Source Code | Yes | The code of our implementation can be found at https://github.com/marek-elias/caching/ |
| Open Datasets | Yes | Bright Kite dataset (Cho et al., 2011) contains data from a certain social network. ... Citi Bike dataset contains data about bike trips in a bike sharing platform Citi Bike. |
| Dataset Splits | No | The paper describes dataset usage and selection criteria, such as "choose instances corresponding to the first 100 users with the longest check-in sequences" and "trimming length of each instance to 25 000", but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing algorithms and using predictors, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Notes on implementation of F&R. We follow the recommendations in Section 3 except that Follower switches to Robust whenever its cost is α = 1 times higher compared to Belady in the same period. With higher α, the performance of F&R approaches Ft P on the considered datasets. With k = 10 (Bright Kite dataset), we use F = [1, 6, 9] corresponding to f(i) = i. Note that, with such small k, polynomial and exponential f would also give a very similar F. With k = 100 (Citi Bike dataset), we use exponential f(i) = 2i+1 1. With a-separated queries, Follower uses LRU heuristic when prediction is unavailable, and Robust ignores F, querying the predictor at each page fault separated from the previous query by at least a time steps. |