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].
On the Generalization of Feature Incremental Learning
Authors: Chao Xu, Xijia Tang, Lijun Zhang, Chenping Hou
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the comprehensive experimental and theoretical results mutually validate each other, underscoring the reliability of our conclusions. . . . Comprehensive experimental results corroborate the theoretical findings, enhancing their reliability and demonstrating the feasibility of applying these theoretical insights to model design. . . . In this section, soft margin SVM [Cortes and Vapnik, 1995] and logistic regression (LR) [Berger et al., 1996] are applied as demonstrations, aiming to form mutual verification through experiments and theories. |
| Researcher Affiliation | Academia | Chao Xu1 , Xijia Tang1 , Lijun Zhang2 and Chenping Hou1 1College of Science, National University of Defense Technology, Changsha, 410073, China. 2Nanjing University, Nanjing, China. EMAIL, {zljzju}@gmail.com |
| Pseudocode | No | The paper describes mathematical formulations and strategies but does not include any explicitly labeled pseudocode blocks or algorithms in a structured, step-by-step format typical of pseudocode. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | We adopt 8 datasets from UCI Repository 1 and LIBSVM Library 2 to carry out the experiments. 1http://archive.ics.uci.edu/ml 2http://www.csie.ntu.edu.tw/~cjlin/libsvm |
| Dataset Splits | Yes | As for the parameters selection of the algorithms, we conduct K-fold cross-validation on the training set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions applying soft margin SVM [Cortes and Vapnik, 1995] and logistic regression (LR) [Berger et al., 1996] but does not specify the versions of any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | As for the parameters selection of the algorithms, we conduct K-fold cross-validation on the training set. Specifically, we use the grid search method to obtain the optimal parameter combination, and the search range of each parameter is 10^-3, 10^-2, 10^-1, 10^0, 10^1, 10^2, 10^3. |