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..
A unified framework for information-theoretic generalization bounds
Authors: Yifeng Chu, Maxim Raginsky
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms. The main technical tool is a probabilistic decorrelation lemma based on a change of measure and a relaxation of Young s inequality in LĪp Orlicz spaces. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801, USA. |
| Pseudocode | No | The paper is highly theoretical and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any open-source code for the methodology described. |
| Open Datasets | No | The paper describes theoretical work and does not use or mention any datasets for training. |
| Dataset Splits | No | The paper describes theoretical work and does not mention any dataset splits for validation. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not involve an experimental setup with hyperparameters or training settings. |