Hypothesis Set Stability and Generalization
Authors: Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a study of generalization for data-dependent hypothesis sets. We give a general learning guarantee for data-dependent hypothesis sets based on a notion of transductive Rademacher complexity. Our main result is a generalization bound for data-dependent hypothesis sets expressed in terms of a notion of hypothesis set stability and a notion of Rademacher complexity for data-dependent hypothesis sets that we introduce. This bound admits as special cases both standard Rademacher complexity bounds and algorithm-dependent uniform stability bounds. We also illustrate the use of these learning bounds in the analysis of several scenarios. |
| Researcher Affiliation | Collaboration | Dylan J. Foster Massachusetts Institute of Technology dylanf@mit.edu Spencer Greenberg Spark Wave admin@sparkwave.tech Satyen Kale Google Research satyen@satyenkale.com Haipeng Luo University of Southern California haipengl@usc.edu Mehryar Mohri Google Research and Courant Institute mohri@google.com Karthik Sridharan Cornell University sridharan@cs.cornell.edu |
| Pseudocode | No | The paper describes theoretical concepts and bounds but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or describe any specific datasets for empirical evaluation, thus no public dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments, thus no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, thus no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, thus no details on experimental setup or hyperparameters are provided. |