Online Knapsack with Frequency Predictions
Authors: Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, we conduct experiments on synthetic data sets, and show our algorithm augmented with frequency predictions considerably outperforms the classic worst-case ZCL algorithm. |
| Researcher Affiliation | Collaboration | Sungjin Im Electrical Engineering and Computer Science University of California, Merced sim3@ucmerced.edu Ravi Kumar Google Research Mountain View, CA ravi.k53@gmail.com Mahshid Montazer Qaem Electrical Engineering and Computer Science University of California, Merced mmontazerqaem@ucmerced.edu Manish Purohit Google Research Mountain View, CA mpurohit@google.com |
| Pseudocode | No | The paper states: 'For clarity, we include a formal description of the algorithm in the Supplementary Material.' This indicates that pseudocode or an algorithm block is not present in the main text. |
| Open Source Code | No | No explicit statement or link is provided that offers concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The experiments are conducted on 'synthetic data sets' generated by the authors, and no concrete access information (e.g., specific link, DOI, repository name, or formal citation to an established public dataset) is provided. |
| Dataset Splits | No | The paper describes generating synthetic data and running experiments, but it does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The knapsack capacity is set to 1. Each item is assumed to have a size 10 4 and an integer value in the range of [vmin = 1, vmax = 100]. For each value v, its frequency lower bound v is sampled from [50, 150] independently and uniformly at random; therefore, in expectation, at least 104 items arrive in an instance. We use a control parameter δ to derive the upper bounds uv from v and set uv to d(1 + δ) ve. For each instance, sv is sampled from [ v, uv] independently and uniformly at random and items arrive in random order. The results are the geometric average over 10 instances. |