From Stochastic Mixability to Fast Rates
Authors: Nishant A Mehta, Robert C. Williamson
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The present paper presents a direct proof of fast rates for ERM in terms of stochastic mixability of (ℓ, F, P), and in so doing provides new insight into the fast-rates phenomenon. The proof exploits an old result of Kemperman on the solution to the general moment problem. |
| Researcher Affiliation | Academia | Nishant A. Mehta Research School of Computer Science Australian National University nishant.mehta@anu.edu.au Robert C. Williamson Research School of Computer Science Australian National University and NICTA bob.williamson@anu.edu.au |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | This is a theoretical paper focusing on proofs and mathematical concepts, and as such, it does not involve the use of empirical datasets for training. |
| Dataset Splits | No | This is a theoretical paper that does not describe empirical experiments, thus no training/validation/test dataset splits are discussed. |
| Hardware Specification | No | This is a theoretical paper and does not involve experimental runs, thus no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper and does not involve experimental implementations, thus no software dependencies with version numbers are specified. |
| Experiment Setup | No | This is a theoretical paper and does not describe any empirical experiments or their setup, thus no hyperparameter values or training configurations are provided. |