Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
From Stochastic Mixability to Fast Rates
Authors: Nishant A Mehta, Robert C. Williamson
NeurIPS 2014 | Venue PDF | 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 EMAIL Robert C. Williamson Research School of Computer Science Australian National University and NICTA EMAIL |
| 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. |