Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Invariant and Transportable Representations for Anti-Causal Domain Shifts
Authors: Yibo Jiang, Victor Veitch
NeurIPS 2022 | Venue PDF | 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). |