Invariant and Transportable Representations for Anti-Causal Domain Shifts
Authors: Yibo Jiang, Victor Veitch
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed learning algorithm. |
| Researcher Affiliation | Collaboration | Yibo Jiang1 and Victor Veitch2,3 1Department of Computer Science, University of Chicago 2Department of Statistics, University of Chicago 3Google Research |
| Pseudocode | No | The paper describes the learning algorithm in detail using text and mathematical equations in Section 4, but it does not include a distinct 'Pseudocode' or 'Algorithm' block or figure. |
| Open Source Code | Yes | Code is available at https://github.com/ybjiaang/ACTIR. |
| Open Datasets | Yes | Color MNIST modifies the original MNIST dataset [Arj+19]. ... The goal of the Camelyon17 dataset [Ban+18] is to predict the existence of a tumor given a region of tissue. |
| Dataset Splits | Yes | We create two training domains with βe 2 {0.95, 0.7}, one validation domain with βe = 0.6 and one test domain with βe = 0.1. (Section 6.1 Synthetic Dataset). ... Following the WILDS benchmark [Koh+21], we use 3 for training, 1 for validation, and the last one for test. (Section 6.3 Camelyon17) |
| Hardware Specification | No | The paper states, 'We also acknowledge the University of Chicago s Research Computing Center for providing computing resources.' (Acknowledgments). This is a general acknowledgment of a computing resource but does not specify any particular hardware models (e.g., GPU type, CPU, or memory details). |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer' and 'Res Net-18 model' but does not specify exact version numbers for these software components or any other libraries used (e.g., 'PyTorch 1.9'). |
| Experiment Setup | Yes | For the fine-tuning test, we run 20 steps with a learning rate 10 2. (Section 6.1 Synthetic Dataset, Section 6.2 Color MNIST). ... We use a three-layer neural network with hidden size 8 and Re LU activation for Φ and train the neural network with Adam optimizer. (Section 6.1 Synthetic Dataset). ... We use a pre-trained Res Net-18 model for our Φ and train the whole model using Adam optimizer with a learning rate 10 4. (Section 6.3 Camelyon17). |