Optimal Prediction of the Number of Unseen Species with Multiplicity
Authors: Yi Hao, Ping Li
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of our approach, we present in this section experimental results on synthetic data (see the plots on the next page), and in the supplementary material ones on real data. |
| Researcher Affiliation | Collaboration | Yi Hao , Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th St. Bellevue, WA 98004, USA yih179@eng.ucsd.edu , pingli98@gmail.com. Yi Hao is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of California, San Diego. Yi Hao s work was conducted while he was a summer intern at Baidu Research-Bellevue. |
| Pseudocode | No | The paper describes the mathematical form of the estimator but does not include a pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions 'synthetic data' with described distributions and 'real data' in the supplementary material, but does not provide specific access information (link, DOI, formal citation) for a publicly available dataset in the main text. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. It describes the parameters for generating synthetic data but not how that data was partitioned for evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific libraries or solvers). |
| Experiment Setup | Yes | Our algorithm has a single hyper-parameter r. In the experiments, we choose r = log(n(a + 1)2/(a 1)). We fix the sample size to be n = S/2, vary a from 1 to 10, and test three different µ values, µ = 1, 2, and 3. |