Active Learning for Decision-Making from Imbalanced Observational Data
Authors: Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of this decision-making aware active learning in two decision-making tasks: in simulated data with binary outcomes and in a medical dataset with synthetic and continuous treatment outcomes. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Aalto University, Espoo, Finland 2Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. |
| Pseudocode | No | The paper does not contain any explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper refers to and links to third-party toolboxes (GPy and Stan) but does not explicitly state that the authors' own implementation code or source code for the methodology described in the paper is available. |
| Open Datasets | Yes | The IHDP data set. We use the Infant Health and Development Program (IHDP) dataset from Hill (2011), also used e.g. by Shalit et al. (2017) and Alaa & van der Schaar (2017), including synthetic outcomes, containing 747 observations of 25 features. |
| Dataset Splits | Yes | We evaluate the performance in leave-one-out cross-validation, but in order to make the problem even more realistically hard, for each of the 747 target units we choose randomly 100 observations as training examples. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | We fit separate GPs to the outcomes of each treatment with GPy1 (version 1.9.2). ... The model is fit using a probabilistic programming language Stan (Stan Development Team, 2017; Carpenter et al., 2017). |
| Experiment Setup | Yes | We use an exponentiated quadratic kernel with a separate length-scale parameter for each variable, and optimize the hyperparameters using marginal likelihood. ... We use Gauss-Hermite quadrature of order 32 to approximate the expectations in D-M aware, Targeted-IG, and EIG. ... Training sample size is 30. ... We assume that the RBF centers and length-scale are known, so that only w0 and w1 need to be learned. |