Online Facility Location with Predictions
Authors: Shaofeng H.-C. Jiang, Erzhi Liu, You Lyu, Zhihao Gavin Tang, Yubo Zhang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, our theoretical analysis is supported by empirical evaluations for the tradeoffs between η and the competitive ratio on various real datasets of different types. |
| Researcher Affiliation | Academia | Shaofeng H.-C. Jiang Peking University Email: shaofeng.jiang@pku.edu.cn Erzhi Liu Shanghai Jiao Tong University Email: lezdzh@sjtu.edu.cn You Lyu Shanghai Jiao Tong University Email: vergil@sjtu.edu.cn Zhihao Gavin Tang Shanghai University of Finance and Economics Email: tang.zhihao@mail.shufe.edu.cn Yubo Zhang Peking University Email: zhangyubo18@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 Prediction-augmented Meyerson |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of its source code. |
| Open Datasets | Yes | In particular, we consider three Euclidean data sets, a) Twitter (Chan et al.), b) Adult (Dua & Graff, 2017), and c) Non Uni (Cebecauer & Buzna, 2018)... and one graph dataset, US-PG (Rossi & Ahmed, 2015) |
| Dataset Splits | Yes | To evaluate the performance of this simple predictor, we take a random sample of 30% points from the dataset as the training set T, and take the remaining 70% as the test set X. |
| Hardware Specification | Yes | All our experiments are conducted on a laptop with Intel Core i7 CPU and 16GB memory. |
| Software Dependencies | No | The paper mentions general tools but does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | Since the algorithms are randomized, we repeat every run 10 times and take the average cost. |