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..
Information-theoretic analysis of generalization capability of learning algorithms
Authors: Aolin Xu, Maxim Raginsky
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive upper bounds on the generalization error of a learning algorithm in terms of the mutual information between its input and output. The bounds provide an information-theoretic understanding of generalization in learning problems, and give theoretical guidelines for striking the right balance between data fit and generalization by controlling the input-output mutual information. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801, USA. |
| Pseudocode | No | The paper describes algorithms such as the Gibbs algorithm and noisy ERM, but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention or provide access to any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with specific datasets, thus no information on training data availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation, so no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup or software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |