Differentiable Model Selection for Ensemble Learning
Authors: James Kotary, Vincenzo Di Vito, Ferdinando Fioretto
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The e2e-CEL training is evaluated on several vision classification tasks: digit classification on MNIST dataset [Deng, 2012], age-range estimation on UTKFace dataset [Zhifei Zhang, 2017], image classification on CIFAR10 dataset [Krizhevsky et al., 2009], and emotion detection on FER2013 dataset [Liu et al., 2016]. Table 2 reports the best accuracy over all the ensemble sizes k of ensembles trained by e2e-CEL along with that of each baseline ensemble model, where each are formed using the same pre-trained base learners. |
| Researcher Affiliation | Academia | James Kotary1 , Vincenzo Di Vito1 and Ferdinando Fioretto1 1 University of Virginia {jkotary, vdivitof}@syr.edu, fioretto@virginia.edu |
| Pseudocode | Yes | Algorithm 1 summarizes the e2e-CEL procedure for training a selection net. Algorithm 1: Training the Selection Net |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | digit classification on MNIST dataset [Deng, 2012], age-range estimation on UTKFace dataset [Zhifei Zhang, 2017], image classification on CIFAR10 dataset [Krizhevsky et al., 2009], and emotion detection on FER2013 dataset [Liu et al., 2016]. |
| Dataset Splits | Yes | In each dataset there is an implied train/test/validation split, so that evaluation of a trained model is always performed on its test portion. Where this distinction is needed, the symbols Xtrain, Xvalid, Xtest are used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments, only stating that the selection net uses the 'same CNN architecture as that of the base learner models'. |
| Software Dependencies | No | The paper refers to 'standard automatic differentiation employed in machine learning libraries [Paszke et al., 2019]' (which cites PyTorch), but no specific version numbers for any software dependencies or libraries are provided. |
| Experiment Setup | No | The paper describes the general approach to training base learners (e.g., specializing on classes) and the selection net's architecture, and mentions `alpha` in Algorithm 1, but it does not provide specific numerical hyperparameter values such as learning rate, batch size, or number of epochs for the main experimental setup. |