Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses
Authors: Shaojie Li, Bowei Zhu, Yong Liu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | One of the central problems of statistical learning theory is quantifying the generalization ability of learning algorithms within a probabilistic framework. Algorithmic stability is a powerful tool for deriving generalization bounds, however, it typically builds on a critical assumption that losses are bounded. In this paper, we relax this condition to unbounded loss functions with subweibull diameter. This gives new generalization bounds for algorithmic stability and also includes existing results of subgaussian and subexponential diameters as specific cases. Furthermore, we provide a refined stability analysis by developing generalization bounds which can be n-times faster than the previous results, where n is the sample size. Our main technical contribution is general concentration inequalities for subweibull random variables, which may be of independent interest. All the proofs are deferred to the Appendix. |
| Researcher Affiliation | Academia | 1Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 2Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It focuses on theoretical derivations and proofs. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it explicitly state that code is released or available. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using specific datasets. It discusses theoretical applications to concepts like 'regularized nearest-neighbor regression' within a 'metric probability space' but does not specify a publicly available dataset with access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data, therefore, no training, validation, or test dataset splits are provided. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not describe any experiments or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not include details on specific software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |