Learning from Biased Data: A Semi-Parametric Approach
Authors: Patrice Bertail, Stephan Clémençon, Yannick Guyonvarch, Nathan Noiry
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Numerical Experiments In this section, we present two numerical experiments which complement the previous theoretical analysis. We start with a simulated dataset where we design both the source and target distributions. We then turn to a more realistic framework: we use the Life Expectancy Dataset and create some distributional shifts on the observations. |
| Researcher Affiliation | Academia | 1Universit e Paris-Nanterre, France 2T el ecom Paris, France. |
| Pseudocode | Yes | Figure 1. The Rw-ERM Algorithm |
| Open Source Code | No | The paper does not explicitly state that the source code for the methodology is openly available or provide a link to it. |
| Open Datasets | No | We use the Life Expectancy Dataset and only keep the Adult Mortality Rate (x1) and the Alcohol Consumption (x2) features in order to predict the Life Expectancy (y) output. The paper does not provide concrete access information (link, DOI, formal citation with author/year) for this dataset. |
| Dataset Splits | No | The paper mentions dividing data into G1 (training/source) and G2 (test set), but does not explicitly describe a separate validation split with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | Popular implementations of ERM-like learning procedures such as scikit-learn (Pedregosa et al., 2018) support a weight option... The version number for scikit-learn is not specified. |
| Experiment Setup | Yes | To estimate α , we implement a gradient descent algorithm to minimize Ψnobs. To avoid getting trapped in potential local minima, we rerun the descent algorithm nboot times using a bootstrapping rationale presented in the Supplementary Material. Among the sequence (α(b))nboot b=1 thus constructed, we select arg minα (α(b)) nboot b=1 Ψnobs(α(b)) as our final estimator ˆα. In the last step, we train several regression-type algorithms (OLS, SVR, RF) on (Zi)nobs i=1 with weights (g(Zi, ˆα))nobs i=1 . ... for the following choices of parameters: nobs = 10, 000, ntest = 500, nrep = 100 and nboot = 100. The SVR algorithm is run for three different values of the parameter C (0.01, 0.1 and 1). |