Few-shot Domain Adaptation by Causal Mechanism Transfer
Authors: Takeshi Teshima, Issei Sato, Masashi Sugiyama
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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). |