Generalization Bounds for Uniformly Stable Algorithms
Authors: Vitaly Feldman, Jan Vondrak
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We substantially improve generalization bounds for uniformly stable algorithms without making any additional assumptions. First, we show that the bound in this setting is O( p(γ + 1/n) log(1/δ)) with probability at least 1 δ. In addition, we prove a tight bound of O(γ2 + 1/n) on the second moment of the estimation error. The best previous bound on the second moment is O(γ + 1/n). Our proofs are based on new analysis techniques and our results imply substantially stronger generalization guarantees for several well-studied algorithms. |
| Researcher Affiliation | Collaboration | Vitaly Feldman Google Brain Jan Vondrak Stanford University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It focuses on theoretical proofs and mathematical derivations. |
| Open Source Code | No | The paper does not provide any concrete access to source code, nor does it state that code will be made available. |
| Open Datasets | No | This is a theoretical paper focused on generalization bounds and proofs. It does not involve the use of specific datasets for training or experimentation, nor does it provide concrete access information for any dataset. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical validation on datasets, therefore, no training/test/validation splits are described. |
| Hardware Specification | No | This is a theoretical paper. No hardware specifications are mentioned as part of any experimental setup. |
| Software Dependencies | No | This is a theoretical paper. No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |