Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$
Authors: Yegor Klochkov, Nikita Zhivotovskiy
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper is entirely focused on presenting and proving mathematical theorems and bounds related to generalization error and excess risk in machine learning algorithms, particularly uniform stability. It contains sections like 'Main results' with Theorem 1.1 and 1.2, and a dedicated 'Proofs' section. There is no mention of experiments, datasets, empirical evaluation, or performance metrics. |
| Researcher Affiliation | Academia | Yegor Klochkov Cambridge-INET, Faculty of Economics University of Cambridge yk376@cam.ac.uk Nikita Zhivotovskiy Department of Mathematics ETH, Zürich nikita.zhivotovskii@math.ethz.ch |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It primarily presents mathematical definitions, theorems, and proofs. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets. Therefore, no information regarding publicly available or open datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets. Therefore, no information regarding training/test/validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments. Therefore, no hardware specifications used for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments or specific implementations. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs. It does not describe any practical experiments or their setup, including hyperparameters or system-level training settings. |