Maxing and Ranking with Few Assumptions
Authors: Moein Falahatgar, Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we validate the performance of our algorithms using simulated data. All results are averaged over 100 runs. |
| Researcher Affiliation | Academia | University of California, San Deigo {moein,yih179,alon,dheerajpv7,vaishakhr}@ucsd.edu |
| Pseudocode | Yes | Algorithm 1 SEQ-ELIMINATE |
| Open Source Code | No | The paper does not include any statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper explicitly states the use of 'simulated data' based on defined stochastic models, but it does not provide access information (link, DOI, repository, or formal citation) for any publicly available or open dataset. |
| Dataset Splits | No | The paper discusses simulated data and error probabilities but does not specify any explicit train/validation/test dataset splits or cross-validation setups. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific solvers). |
| Experiment Setup | Yes | We use maxing algorithms to find 0.05-maximum with error probability δ = 0.1. Note that i = 1 is the unique 0.05-maximum under this model. In Figure 1, we compare the performance of SEQ-ELIMINATE and OPT-MAXIMIZE over different ranges of n. Figures 1(a), 1(b) show that for small n i.e., n 1300 SEQ-ELIMINATE performs well and for large n i.e., n 1300, OPT-MAXIMIZE performs well. Since we are using δ = 0.1, the experiment suggests that for δ 1 n1 3 , OPT-MAXIMIZE uses fewer comparisons as compared to SEQ-ELIMINATE. In further experiments, we use δ = 0.1 and n < 1000 so we use SEQ-ELIMINATE for better performance. |