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..
Generalizing across Temporal Domains with Koopman Operators
Authors: Qiuhao Zeng, Wei Wang, Fan Zhou, Gezheng Xu, Ruizhi Pu, Changjian Shui, Christian Gagné, Shichun Yang, Charles X. Ling, Boyu Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through empirical evaluations conducted on synthetic and real-world datasets, we validate the effectiveness of our proposed approach. |
| Researcher Affiliation | Academia | 1University of Western Ontario 2Beihang University 3Vector Institute 4Universit e Laval |
| Pseudocode | Yes | Algorithm 1: TKNets (one episode) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluated our algorithm on 6 datasets, 4 of which were collected in real-world scenarios (RMNIST (Ghifary et al. 2015), Portrait (Kumar, Ma, and Liang 2020; Chen and Chao 2021), Cover Type (Kumar, Ma, and Liang 2020)), and FMo W (Christie et al. 2018)). |
| Dataset Splits | No | The paper describes how data is divided into domains and how support/query sets are used during training and testing (e.g., 'randomly choose the data sets Si, Si+1 from two consecutive domains as support and query sets'), but it does not provide explicit training, validation, and test split percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'All the baselines and experiments were implemented with Domain Bed package (Gulrajani and Lopez-Paz 2020)', but it does not specify a version number for this package or any other software dependencies. |
| Experiment Setup | No | The paper describes the sampling strategy for support and query sets during training ('sample NB samples from each class k in Si') and mentions that experiments were implemented with 'Domain Bed package (Gulrajani and Lopez-Paz 2020) under the same setting', but it does not explicitly list concrete hyperparameter values such as learning rate, batch size, or number of epochs within the main text. |