Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation
Authors: Elbert Du, Franklyn Wang, Michael Mitzenmacher
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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 wang@college.havard.edu>, Elbert Du <edu@college.havard.edu>. |
| 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. |