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..
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Authors: Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu, Yefeng Zheng
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on real-world datasets shows that our CVIB significantly enhances both shallow and deep models |
| Researcher Affiliation | Collaboration | Zifeng Wang1,Xi Chen2,Rui Wen2,Shao-Lun Huang1,Ercan E. Kuruoglu1,3,Yefeng Zheng2 1Tsinghua-Berkeley Shenzhen Institute, Tsinghua University 2Jarvis Lab, Tencent 3Institute of Science and Technologies of Information, CNR, Pisa, Italy |
| Pseudocode | Yes | Algorithm 1 Counterfactual Learning with CVIB in MNAR Data for Recommendation. |
| Open Source Code | Yes | Code is available at https://github.com/Ryan Wang Zf/CVIB-Rec. |
| Open Datasets | Yes | Yahoo! R3 Dataset [25]. This is a user-song rating dataset... Coat Shopping Dataset [30]. This dataset consists of... |
| Dataset Splits | Yes | We randomly draw 30% data from the training set for validation, on which we apply a grid search for hyperparameters to pick the best configuration. |
| Hardware Specification | No | The paper does not specify the exact GPU models, CPU models, or other detailed hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch [26]' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | For both the MF and NCF, we fix the embedding size of both users and items to be 4... Adam [20] is utilized as the optimizer for fast convergence during training, with its learning rate in {0.1, 0.05, 0.01, 0.005, 0.001}, weight decay in {10 3, 10 4, 10 5}, and batch size in {128, 256, 512, 1024, 2048}. For NCF, we set an additional hidden layer with width 8. Specifically for CVIB, we set the hyperparameters α {2, 1, 0.5, 0.1}, and γ {1, 0.1, 10 2, 10 3}. |