The Falling Factorial Basis and Its Statistical Applications
Authors: Yu-Xiang Wang, Alex Smola, Ryan Tibshirani
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide no theory but demonstrate excellent empirical results, improving on, e.g., the maximum mean discrepancy (Gretton et al., 2012) and Anderson-Darling (Anderson & Darling, 1954) tests. and 5.2. Numerical experiments We examine the higher order KS tests by simulation. |
| Researcher Affiliation | Academia | Yu-Xiang Wang YUXIANGW@CS.CMU.EDU Alex Smola ALEX@SMOLA.ORG Ryan J. Tibshirani RYANTIBS@STAT.CMU.EDU Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213 |
| Pseudocode | Yes | Algorithm 1 Multiplication by H(k) and Algorithm 2 Multiplication by (H(k)) 1 |
| Open Source Code | No | The paper mentions that their implementation of falling factorial transforms uses MEX functions for comparison with highly-optimized libraries, implying custom code, but does not explicitly state that this code is open-sourced or provide a link for it. It only states the paper and supplement are on arXiv. |
| Open Datasets | No | The experiments use synthetically generated data drawn from standard statistical distributions (Normal, t-distribution, Laplace) rather than pre-existing publicly available datasets with specific access information like links or citations. |
| Dataset Splits | No | The paper describes generating synthetic data for numerical experiments but does not provide specific dataset split information (e.g., percentages or counts for train/validation/test sets), cross-validation setup, or citations to predefined splits. |
| Hardware Specification | No | The paper states: 'The experiments were performed on a laptop computer.' This is not a specific hardware detail like a GPU/CPU model or memory amount. |
| Software Dependencies | No | The paper mentions 'Matlab' and 'Stanford Wave Lab' functions, but does not provide specific version numbers for these software components or any other key dependencies. |
| Experiment Setup | Yes | The paper states: 'Figures 3 and 4 show the results of two experiments in which n = 100 and R = 1000.' It also describes the setup for numerical experiments: 'The setup: we fix two distributions P, Q. We draw n i.i.d. samples X(n), Y(n) P, calculate a test statistic, and repeat this R/2 times; we also draw n i.i.d. samples X(n) P, Y(n) Q, calculate a test statistic, and repeat R/2 times.' |