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 [1].
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. |