PAC-Bayes Tree: weighted subtrees with guarantees
Authors: Tin D. Nguyen, Samory Kpotufe
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments on real-world datasets, for two common partition-tree approaches, dyadic trees and KD-trees. The various datasets are described in Table 1. |
| Researcher Affiliation | Academia | Tin Nguyen MIT EECS tdn@mit.edu Samory Kpotufe Princeton University ORFE samory@princeton.edu |
| Pseudocode | Yes | Algorithm 1 Bottom-up pass; Algorithm 2 Top-down pass |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | Table 1: UCI datasets (Name (abbreviation) Features count Labels count Train size: Spambase, EEG Eye State, Epileptic Seizure Recognition, Crowdsourced Mapping, Wine Quality, Optical Recognition of Handwritten Digits, Letter Recognition). |
| Dataset Splits | Yes | Testing data is fixed to be of size 2000, while each experiment is ran 5 times (with random choice of training data of size reported in Table 1) and average performance is reported. In each experiment, all parameters are chosen by 2-fold cross-validation for each of the procedures. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers, such as programming language versions or library versions (e.g., PyTorch, TensorFlow, scikit-learn). |
| Experiment Setup | No | The paper describes that parameters are chosen by 2-fold cross-validation with specific search grids (log-grid from 2^-8 to 2^6 and linear-grid search), but it does not provide the concrete final hyperparameter values or other system-level training settings (e.g., batch size, optimizer) used for the reported results. |