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..
Few-shot Domain Adaptation by Causal Mechanism Transfer
Authors: Takeshi Teshima, Issei Sato, Masashi Sugiyama
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate the effectiveness of the proposed algorithm (Section 6). |
| Researcher Affiliation | Academia | 1The University of Tokyo, Tokyo, Japan 2RIKEN, Tokyo, Japan. |
| Pseudocode | Yes | Algorithm 1 Proposed method: mechanism transfer |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We use the gasoline consumption data (Greene, 2012, p.284, Example 9.5), which is a panel data of gasoline usage in 18 of the OECD countries over 19 years. |
| Dataset Splits | Yes | To perform hyperparameter selection as well as early-stopping, we record the leave-one-out cross-validation (LOOCV) mean-squared error on the target training data every 20 epochs and select its minimizer. |
| Hardware Specification | Yes | All experiments were conducted on an Intel Xeon(R) 2.60 GHz CPU with 132 GB memory. |
| Software Dependencies | No | The paper mentions 'Python using the Py Torch library (Paszke et al., 2019) or the R language (R Core Team, 2018)' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma & Ba, 2017) with fixed parameters (β1, β2, ϵ) = (0.9, 0.999, 10 8), fixed initial learning rate 10 3, and the maximum number of epochs 300. The number of hidden units for ψd is chosen from {10, 20} and the coefficient of weightdecay from 10{ 2, 1}. The ℓ2 regularization coefficient λ of KRR is chosen from λ 2{ 10,...,10} following Cortes et al. (2019). |