Multivariate f-divergence Estimation With Confidence
Authors: Kevin Moon, Alfred Hero
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our theoretical results and, as an illustration, use them to empirically bound the best achievable classification error. We first apply the weighted ensemble estimator of divergence to simulated data to verify the central limit theorem. We then use the estimator to obtain confidence intervals on the error exponents of the Bayes probability of error for the Iris data set from the UCI machine learning repository [33, 34]. |
| Researcher Affiliation | Academia | Kevin R. Moon Department of EECS University of Michigan Ann Arbor, MI krmoon@umich.edu Alfred O. Hero III Department of EECS University of Michigan Ann Arbor, MI hero@eecs.umich.edu |
| Pseudocode | Yes | Algorithm 1 Optimally weighted ensemble divergence estimator |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code is available. |
| Open Datasets | Yes | We then use the estimator to obtain confidence intervals on the error exponents of the Bayes probability of error for the Iris data set from the UCI machine learning repository [33, 34]. |
| Dataset Splits | Yes | We compared the bounds to the performance of a quadratic discriminant analysis classifier (QDA) with 5-fold cross validation. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types) used for the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | In practice, we estimate cα(f1||f2) for multiple values of α (e.g. 0.01, 0.02, . . . , 0.99) and choose the minimum. We estimated a bound on the pairwise Bayes error... and used bootstrapping to calculate confidence intervals. |