Generalization Analysis on Learning with a Concurrent Verifier
Authors: Masaaki Nishino, Kengo Nakamura, Norihito Yasuda
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper gives a generalization analysis of learning with a CV. We analyze how the learnability of a machine learning model changes with a CV and show a condition where we can obtain a guaranteed hypothesis using a verifier only in the inference time. We also show that typical error bounds based on Rademacher complexity will be no larger than that of the original model when using a CV in multi-class classification and structured prediction settings. This paper gives theoretical analyses of the generalization errors of a machine learning model with a CV. |
| Researcher Affiliation | Industry | Masaaki Nishino, Kengo Nakamura, Norihito Yasuda NTT Communication Science Laboratories, NTT Corporation {masaaki.nishino.uh, kengo.nakamura.dx, norihito.yasuda.hn}@hco.ntt.co.jp |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for the methodology or analysis described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using specific datasets; it refers to 'training data S' only in a definitional context for theoretical analysis, not for practical implementation or public access. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies, thus no specific dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any computational experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational implementation details or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not detail any experimental setup, hyperparameters, or system-level training settings. |