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..
Competitive Caching with Machine Learned Advice
Authors: Thodoris Lykouris, Sergei Vassilvtiskii
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our results with an empirical evaluation of our algorithm on real world datasets, and show that it performs well empirically even using simple off-the-shelf predictions. |
| Researcher Affiliation | Collaboration | 1Cornell University, Ithaca, NY, USA 2Google Research, New York, NY, USA. Correspondence to: Thodoris Lykouris <EMAIL>, Sergei Vassilvitskii <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Predictive Marker with oracle-based and random tie-breaking based on clean chains |
| Open Source Code | No | The paper does not contain an explicit statement that the authors are releasing their source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | BK is data extracted from Bright Kite, a now defunct social network... This dataset is publicly available at (Cho et al., 2011; Bri). Citi is data extracted from Citi Bike... The dataset is publicly available at (Cit). |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. It mentions using real-world datasets but does not detail how these datasets were partitioned for evaluation or training purposes. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | No | The paper mentions setting 'k' (cache size) for experiments (e.g., "We set k = 10", "k = 100"). However, it does not provide comprehensive experimental setup details such as hyperparameters, optimizer settings, training configurations, or system-level settings typically found in machine learning experiments. |