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..
Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation
Authors: Elbert Du, Franklyn Wang, Michael Mitzenmacher
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the theoretical results with synthetic experiments. In what follows, there are n = 10^6 keys, and the frequency of the kth key is n/k. We consider three different scenarios: No Screening, Perfect Screening, Imperfect Screening. ... We use the CAIDA dataset. |
| Researcher Affiliation | Academia | 1Department of Mathematics, Harvard University 2Department of Computer Science, Harvard University. Correspondence to: Franklyn Wang <franklyn EMAIL>, Elbert Du <EMAIL>. |
| Pseudocode | Yes | We reproduce the algorithm of (Hsu et al., 2019) below in Algorithm 1. |
| Open Source Code | Yes | Our source code can be found at https://github.com/franklynwang/ putting-the-learning-in-LAA. |
| Open Datasets | Yes | Following (Hsu et al., 2019), we use the CAIDA dataset. |
| Dataset Splits | Yes | We use the first 7 minutes of the link for training, the 8th minute for validation, and the 9th, 30th, and 60th minutes for testing. |
| Hardware Specification | Yes | We use 1 NVIDIA V100 for each run. |
| Software Dependencies | No | The paper mentions using "an LSTM" and "Adam" as the optimizer, but it does not specify software versions (e.g., PyTorch version, Python version, CUDA version, etc.). |
| Experiment Setup | Yes | Each training run takes around 12 to 18 hours, and trains for 200 epochs with batch size equal to 1024. For the optimizer, we use Adam with learning rate 10^-3. |