Sequential Mode Estimation with Oracle Queries
Authors: Dhruti Shah, Tuhinangshu Choudhury, Nikhil Karamchandani, Aditya Gopalan5644-5651
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report numerical simulation results that support our theoretical query complexity performance bounds. |
| Researcher Affiliation | Academia | 1Indian Institute of Technology, Bombay 2Indian Institute of Science, Bangalore |
| Pseudocode | Yes | Algorithm 1 Mode estimation algorithm under QM1; Algorithm 2 Mode estimation algorithm under QM2 |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of its source code. |
| Open Datasets | Yes | Real world dataset: As mentioned in the introduction, one of the applications of mode estimation is partial clustering. Via experiments on a real-world purchase data set (Leskovec and Krevl 2014), we were able to benchmark the performance of our proposed Algorithm 2 for pairwise queries... |
| Dataset Splits | No | The paper does not explicitly describe validation dataset splits, only mentioning training and testing implicitly or through a dataset citation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | Yes | For both the QM1 and QM2 models, we simulate Algorithm 1 and Algorithm 2 for various synthetic distributions. We take k = 5120 and keep the difference p1 p2 = 0.208 constant for each distribution. For the other pi s we follow two different models: 1. Uniform distribution : The other pi s for i = 3....k are chosen such that each pi = 1 p1 p2 2. Geometric distribution : The other pi s are chosen such that p2, p3....pk form a decreasing geometric distribution which sums upto 1 p1. For each distribution we run the experiment 50 times and take an average to plot the query complexity. ... With a target confidence of 99%, our proposed algorithm terminates in 631k pairwise queries... |