Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Partial Transportability for Domain Generalization
Authors: Kasra Jalaldoust, Alexis Bellot, Elias Bareinboim
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 38,160 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,895 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL (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) |