Partial Transportability for Domain Generalization
Authors: Kasra Jalaldoust, Alexis Bellot, Elias Bareinboim
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results are corroborated with experiments. (Abstract) This section illustrates Algs. 1 and 2 for the evaluation and optimization of the generalization error on several tasks, ranging from simulated examples to semi-synthetic image datasets. (Section 5) |
| Researcher Affiliation | Collaboration | Kasra Jalaldoust Alexis Bellot : Elias Bareinboim Causal Artificial Intelligence Lab Columbia University {kasra, eb}@cs.columbia.edu, abellot95@gmail.com (First page) Equal Contribution. :Now at Google Deep Mind. (First page) |
| Pseudocode | Yes | Algorithm 1 Neural-TR (Section 4.1) Algorithm 2 CRO (Causal Robust Optimization) (Section 4.2) |
| Open Source Code | Yes | Code is provided. (NeurIPS Paper Checklist, Section 5) |
| Open Datasets | Yes | Our second experiment considers the colored MNIST (CMNIST) dataset that is used in the literature to highlight the robustness of classifiers to spurious correlations, e.g. see [2]. (Section 5.2) |
| Dataset Splits | No | The paper mentions 'We use data drawn from P 1,2pz, yq to train predictors' (Section 5.2) but does not specify how this data is split into train/validation/test sets for their experiments. |
| Hardware Specification | Yes | All experiments were executed on a Macbook Pro M2 32 GB RAM. (NeurIPS Paper Checklist, Section 8) |
| Software Dependencies | No | For the synthetic experiments, we used feed-forward neural networks... We used Adam optimizer for training the Neural networks. In CMNIST example, we used a standard implementation of a conditional GAN [23] trained over 200 epochs with a batch-size of 64. (Appendix B.3) No specific versions of libraries (e.g., PyTorch, TensorFlow) or Python are mentioned. |
| Experiment Setup | Yes | For the synthetic experiments, we used feed-forward neural networks with 7 layers and 128 ˆ 128 neurons in each layer. The activation for all layers is Re Lu, but for the last layer which is a sigmoid since fθV outputs the probability of V 1. For evaluation, at each epoch, we used 1000 samples from the joint distribution. ... The learning rate of Adam was set to 0.0002. (Appendix B.3) |