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..
Ensemble Pruning for Out-of-distribution Generalization
Authors: Fengchun Qiao, Xi Peng
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on common benchmarks demonstrate the superiority of our approach in both multiand single-source Oo D generalization. |
| Researcher Affiliation | Academia | 1Deep REAL Lab, Department of Computer and Information Sciences, University of Delaware, DE, USA. Correspondence to: Xi Peng <EMAIL>. |
| Pseudocode | No | The paper describes mathematical formulations and procedural steps but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The source codes are publicly available at: https://github.com/joffery/TEP. |
| Open Datasets | Yes | We evaluate our method on the common Oo D generalization benchmark Domain Bed (Gulrajani & Lopez-Paz, 2020). VLCS contains photographic images from four domains: Caltech101, Label Me, SUN09, and VOC2007. Terra Incognita consists of photos of wild animals captured by camera traps at four different locations. |
| Dataset Splits | Yes | Following (Gulrajani & Lopez-Paz, 2020), we use a validation set selected from the training domains for model selection and all the experimental results are averaged over 3 trials. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running its experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions using ResNet-50 and standard SDP solvers but does not provide specific software library names with their version numbers. |
| Experiment Setup | Yes | We empirically set λ = 1 for all experiments. Following (He et al., 2024), we set K = N/2 for all experiments. |