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..
Relative Deviation Margin Bounds
Authors: Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a series of new and more favorable margin-based learning guarantees that depend on the empirical margin loss of a predictor. We give two types of learning bounds, in terms of either the Rademacher complexity or the empirical ℓ covering number of the hypothesis set used, both distribution-dependent and valid for general families. Furthermore, using our relative deviation margin bounds, we derive distribution-dependent generalization bounds for unbounded loss functions under the assumption of a finite moment. We also briefly highlight several applications of these bounds and discuss their connection with existing results. |
| Researcher Affiliation | Collaboration | Corinna Cortes 1 Mehryar Mohri 1 2 Ananda Theertha Suresh 1 1Google Research, New York, NY; 2Courant Institute of Mathematical Sciences, New York, NY;. |
| Pseudocode | No | The paper presents mathematical proofs and theoretical derivations. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper describes theoretical learning bounds and does not mention releasing any open-source code for an implemented methodology. |
| Open Datasets | No | The paper is theoretical and focuses on deriving learning bounds. It does not describe experiments that involve training on a public dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve practical experiments with datasets, and thus, no training/validation/test splits are mentioned. |
| Hardware Specification | No | This is a theoretical paper that does not involve computational experiments, so no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper that does not conduct experiments, and therefore, no software dependencies with version numbers are listed. |
| Experiment Setup | No | This is a theoretical paper presenting new bounds and proofs. It does not describe any experimental setup details such as hyperparameters or training configurations. |