Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
AdaO2B: Adaptive Online to Batch Conversion for Out-of-Distribution Generalization
Authors: Xiao Zhang, Sunhao Dai, Jun Xu, Yong Liu, Zhenhua Dong
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 16,081 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,430 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results have demonstrated that Ada O2B significantly outperforms state-of-the-art baselines on both synthetic and real-world recommendation datasets. |
| Researcher Affiliation | Collaboration | Xiao Zhang,1 Sunhao Dai,1 Jun Xu,1,* Yong Liu,1 Zhenhua Dong2 1 Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 2 Huawei Noah s Ark Lab, Shenzhen, China EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: Ada O2B |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. It mentions a dataset website. |
| Open Datasets | Yes | We used the Kuai Rec dataset3, which provides a fully observed user-item interaction matrix from the popular videosharing app Kuaishou. 3https://kuairec.com |
| Dataset Splits | No | For both synthetic and real-world datasets, we split them into two subsets for the online learning phase (as well as the O2B conversion phase) and the batch testing phase, respectively, denoted by OL-Data and BT-Data. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for its experiments, such as specific GPU/CPU models or memory details. |
| Software Dependencies | No | The paper mentions 'Adam (Kingma and Ba 2014) is used to conduct the optimization' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We trained Ada O2B based on the last 10 (i.e.,, K = 10) data buffers and history policies. We tuned the hyper-parameters as follows: the learning rate was tuned within the range of {1e 2, 1e 3, 1e 4, 1e 5}, the weight decay was tuned among {1e 3, 1e 4, 1e 5, 1e 6}, and the batch size was tuned in {256, 512, 1024, 2048}. |