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 [1].
Multi-task Causal Learning with Gaussian Processes
Authors: Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez, Javier González
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test both the quality of its predictions and its calibrated uncertainties. Compared to single-task models, DAG-GP achieves the best fitting performance in a variety of real and synthetic settings. In addition, it helps to select optimal interventions faster than competing approaches when used within sequential decision making frameworks, like active learning or Bayesian optimization. |
| Researcher Affiliation | Collaboration | Virginia Aglietti University of Warwick The Alan Turing Institute EMAIL Theodoros Damoulas University of Warwick The Alan Turing Institute EMAIL Mauricio A. Álvarez University of Sheffield EMAIL Javier González Microsoft Research Cambridge EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | Yes | Code and data for all the experiments is provided at https://github.com/VirgiAgl/DAG-GP. |
| Open Datasets | Yes | DAG3 is taken from [33] and [13] and is used to model the causal effect of statin drugs on the levels of prostate specific antigen (PSA). |
| Dataset Splits | No | The paper mentions using 'observational dataset DO' and 'interventional dataset DI' of varying sizes and initializations, but it does not specify explicit training, validation, and test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | No | The paper mentions setting the size of the DI dataset for different DAGs (e.g., 'size of DI to 5 |T|'), and states that 'Implementation details are given in the supplement', but it does not provide specific hyperparameters or system-level training settings in the main text. |