Ensembles of Locally Independent Prediction Models
Authors: Andrew Ross, Weiwei Pan, Leo Celi, Finale Doshi-Velez5527-5536
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Across a variety of synthetic and real-world tasks, we find that our method improves generalization and diversity in qualitatively novel ways, especially under data limits and covariate shift. |
| Researcher Affiliation | Academia | Andrew Slavin Ross,1 Weiwei Pan,1 Leo Anthony Celi,2 Finale Doshi-Velez1 1Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA 2Massachusetts Institute of Technology, Cambridge, MA, 02139, USA |
| Pseudocode | No | The paper includes mathematical formulations and descriptions of procedures, but it does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code to replicate all experiments is available at https://github.com/dtak/lit. |
| Open Datasets | Yes | Next, we test our method on several standard binary classification datasets from the UCI and MOA repositories (Lichman 2013; Bifet et al. 2010). These are mushroom, ionosphere, sonar, spectf, and electricity... |
| Dataset Splits | Yes | For all datasets, we randomly select 80% of the dataset for training and 20% for test, then take an additional 20% split of the training set to use for validation. |
| Hardware Specification | No | The paper states that models were 'trained in TensorFlow' but does not provide any specific details about the hardware used, such as GPU/CPU models, memory, or cloud computing resources. |
| Software Dependencies | No | The paper mentions training models in 'TensorFlow' but does not specify a version number for TensorFlow or any other software dependencies, libraries, or packages used in the experiments. |
| Experiment Setup | Yes | For the experiments that follow, we use 256-unit single hidden layer fully connected neural networks with rectifier activations, trained in Tensorflow with Adam. For the real-data experiments, we use dropout and L2 weight decay with a penalty of 0.0001. |