Competitive Caching with Machine Learned Advice
Authors: Thodoris Lykouris, Sergei Vassilvtiskii
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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 <teddlyk@cs.cornell.edu>, Sergei Vassilvitskii <sergeiv@google.com>. |
| 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. |