R-divergence for Estimating Model-oriented Distribution Discrepancy
Authors: Zhilin Zhao, Longbing Cao
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the test power across various unsupervised and supervised tasks and find that R-divergence achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Zhilin Zhao Longbing Cao Data Science Lab, School of Computing & Data X Research Centre Macquarie University, Sydney, NSW 2109, Australia |
| Pseudocode | Yes | The procedure of estimating the model-oriented discrepancy between two probability distributions is summarized in Algorithm 1 (see Appendix B.1). |
| Open Source Code | Yes | The source code is publicly available at: https://github.com/Lawliet-zzl/R-div. |
| Open Datasets | Yes | Benchmark Datasets: Following the experimental setups as [41] and [62], we adopt four benchmark datasets, namely Blob, HDGM, HIGGS, and MNIST. |
| Dataset Splits | Yes | For fair comparisons, we follow the same setup as [62] for all methods. Because all the compared methods have hyper-parameters, we evenly split each dataset into a training set and a validation set to tune hyper-parameters and compute the final test power, respectively. |
| Hardware Specification | No | The paper mentions using specific network architectures (e.g., Res Net18, Alex Net) but does not provide any details about the hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not provide specific version numbers for any programming languages, libraries, or frameworks used. |
| Experiment Setup | Yes | In the pretraining and retraining processes, the learning rate [60] starts at 0.1 and is divided by 10 after 100 and 150 epochs. The batch size is 128, and the number of epochs is 200. |