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].
Towards Improved Risk Bounds for Transductive Learning
Authors: Bowei Zhu, Shaojie Li, Yong Liu
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive new version of concentration inequality for empirical processes in transductive learning and apply generic chaining technique to relax the assumptions and gain tighter results in empirical risk minimization. Furthermore, we concentrate on the generalization of moment penalization algorithm. We design a novel estimator based on the second moment (variance) penalization and derive its learning rates, which is the first theoretical generalization analysis considering variance-based algorithms. |
| Researcher Affiliation | Academia | 1Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China and EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It presents definitions, theorems, lemmas, and theoretical analyses. |
| Open Source Code | No | The paper does not provide any concrete statements about the release of source code, nor does it include links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical derivations and does not describe experiments using specific datasets. While it mentions applications in areas like graph neural networks and semi-supervised learning, it does not use any concrete datasets for empirical evaluation, nor does it provide access information for any such datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments requiring dataset splits. Therefore, no information on training/test/validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments or implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe any empirical experiments, thus no experimental setup details, hyperparameters, or training configurations are provided. |