Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
Authors: Takuo Matsubara
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the performance of the WGBoost algorithm through three experiments using real-world tabular data. |
| Researcher Affiliation | Academia | Takuo Matsubara The University of Edinburgh Edinburgh, EH9 3JZ takuo.matsubara@ed.ac.uk |
| Pseudocode | Yes | Algorithm 1: Wasserstein Gradient Boosting; Algorithm 2: Wasserstein-Boosted Evidential Learning |
| Open Source Code | Yes | The source code is available in https://github.com/takuomatsubara/WGBoost. |
| Open Datasets | Yes | The benchmark protocol uses real-world tabular datasets from the UCI machine learning repository [54] |
| Dataset Splits | Yes | where we held out 20% of the training set as a validation set and chose the number 1 M 4000 achieving the least validation error. |
| Hardware Specification | Yes | All the experiments were performed with x86-64 CPUs, where some of them were parallelised up to 10 CPUs and the rest uses 1 CPU. |
| Software Dependencies | No | The paper mentions the use of software like XGBoost and LightGBM and discusses various algorithms, but it does not specify exact version numbers for any software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | Common Hyperparameters Throughout, we set the number of output particles N to 10 and set each weak learner f to the decision tree regressor [50] with maximum depth 1 for Section 4.1 and 3 for the rest. We set the learning rate ν to 0.1 for regression and 0.4 for classification, unless otherwise stated. |