Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
Authors: Ziyan Wang, Hao Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in several real-world datasets show that our VIR can outperform state-of-the-art imbalanced regression models in terms of both accuracy and uncertainty estimation. |
| Researcher Affiliation | Academia | Ziyan Wang Georgia Institute of Technology wzy@gatech.edu Hao Wang Rutgers University hw488@cs.rutgers.edu |
| Pseudocode | No | The paper describes methods using prose and mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | Code will soon be available at https: //github.com/Wang-ML-Lab/variational-imbalanced-regression. |
| Open Datasets | Yes | We evaluate our methods in terms of prediction accuracy and uncertainty estimation on four imbalanced datasets1, Age DB-DIR [30], IMDB-WIKI-DIR [33], STS-B-DIR [7], and NYUD2-DIR [35]. |
| Dataset Splits | Yes | Age DB-DIR: We use Age DB-DIR constructed in DIR [49], which contains 12.2K images for training and 2.1K images for validation and testing. |
| Hardware Specification | No | The paper mentions receiving 'Amazon Web Service for providing cloud computing credit' but does not provide specific hardware details like GPU or CPU models used for the experiments. |
| Software Dependencies | No | We use Py Torch to implement our method. |
| Experiment Setup | Yes | We use the Adam optimizer [24] to train all models for 100 epochs, with same learning rate and decay by 0.1 and the 60-th and 90-th epoch, respectively. In order to determine the optimal batch size for training, we try different batch sizes and corroborate the conclusion from [49], i.e., the optimal batch size is 256 when other hyperparameters are fixed. |