Learning-Augmented Data Stream Algorithms
Authors: Tanqiu Jiang, Yi Li, Honghao Lin, Yisong Ruan, David P. Woodruff
ICLR 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our results, demonstrating also our improvements in practice. We conduct experiments for the distinct elements and the Fp moment (p > 2) problems, on both real-world and synthetic data, which demonstrate significant practical benefits. |
| Researcher Affiliation | Academia | Tanqiu Jiang Department of Electrical and Computer Engineering Lehigh University Bethlehem, PA 18015, USA EMAIL, Yi Li School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 EMAIL, Honghao Lin Zhiyuan College Shanghai Jiao Tong University Shanghai, China 200240 honghao EMAIL, Yisong Ruan Department of Software engineering Xiamen University Xiamen, Fujian, China 361000 EMAIL, David P. Woodruff Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213, USA EMAIL |
| Pseudocode | No | The paper describes algorithms such as ROUGHL0ESTIMATOR and EXACTCOUNT in textual form but does not provide pseudocode or formally labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | The traffic data is collected at a backbone link of a Tier1 ISP between Chicago and Seattle in 2016 (CAIDA). http://www.caida.org/data/monitors/ passive-equinix-chicago.xml. |
| Dataset Splits | Yes | They use the first 7 minutes for training, the following minute for validation, and estimate the packet counts in subsequent minutes. [...] They use the first 5 days for training, the following day for validation, and estimate the number of times different search queries appear in subsequent days. |
| Hardware Specification | No | The paper does not specify the exact hardware components (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper describes the use of algorithms and models (e.g., RNNs, LSTM) but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For ROUGHL0ESTIMATOR, we set c = 10 and η = 1/4. We use the heavy hitter oracle to predict whether the coordinate will be larger than 210. We randomly select a prime from [11, 31] for the hash buckets. [...] We plot the results for different values of k = 10, 20, 30. |