Exponential Concentration of a Density Functional Estimator
Authors: Shashank Singh, Barnabas Poczos
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For densities on the d-dimensional unit cube [0, 1]d that lie in a β-H older smoothness class, we prove our estimator converges at the rate O n β β+d . Furthermore, we prove the estimator is exponentially concentrated about its mean, whereas most previous related results have proven only expected error bounds on estimators. Our main contribution is to derive convergence rates and an exponential concentration inequality for a particular, consistent, nonparametric estimator for large class of density functionals, including conditional density functionals. |
| Researcher Affiliation | Academia | Shashank Singh Statistics & Machine Learning Departments Carnegie Mellon University Pittsburgh, PA 15213 sss1@andrew.cmu.edu Barnab as P oczos Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 bapoczos@cs.cmu.edu |
| Pseudocode | No | The paper describes mathematical derivations and estimators but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not mention the use of any datasets, public or otherwise, for training. |
| Dataset Splits | No | This is a theoretical paper and does not mention any dataset splits for validation or training. |
| Hardware Specification | No | This is a theoretical paper and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details, hyperparameters, or training configurations. |