Learning to Maximize Mutual Information for Dynamic Feature Selection
Authors: Ian Connick Covert, Wei Qiu, Mingyu Lu, Na Yoon Kim, Nathan J White, Su-In Lee
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments evaluate the proposed method on numerous datasets, and the results show that it outperforms many recent dynamic and static feature selection methods. |
| Researcher Affiliation | Academia | 1Paul G. Allen School of Computer Science & Engineering, University of Washington 2Department of Emergency Medicine, University of Washington. |
| Pseudocode | Yes | Algorithm 1: Training pseudocode |
| Open Source Code | Yes | Code for reproducing our experiments is available online: https://github.com/iancovert/ dynamic-selection. |
| Open Datasets | Yes | Next, we conducted experiments using three publicly available tabular datasets: spam classification (Dua & Graff, 2017), particle identification (Mini Boo NE) (Roe et al., 2005) and diabetes diagnosis (Miller, 1973). |
| Dataset Splits | Yes | We used the standard train-test splits, and we split the train set to obtain a validation set with the same size as the test set (10,000 examples). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Py Torch' and the 'Captum package' but does not specify their exact version numbers (e.g., PyTorch 1.9) used in the experiments. |
| Experiment Setup | Yes | The dropout probability is set to 0.3, the networks have two hidden layers of width 128, and we performed early stopping using the validation loss. For MNIST, we used fully connected architectures with two layers of width 512 and the dropout probability set to 0.3. For CIFAR-10, we used a shared Res Net backbone (He et al., 2016b) consisting of several residually connected convolutional layers. |