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].
Bagging Provides Assumption-free Stability
Authors: Jake A. Soloff, Rina Foygel Barber, Rebecca Willett
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results validate our findings, showing that bagging successfully stabilizes even highly unstable base algorithms. [...] In this section, we study the stability of subbagging in simulation experiments. We use scikitlearn (Pedregosa et al., 2011) for all base algorithms. |
| Researcher Affiliation | Academia | Jake A. Soloff EMAIL Rina Foygel Barber EMAIL Department of Statistics University of Chicago 5747 S Ellis Ave Chicago, IL 60637, USA Rebecca Willett EMAIL Departments of Statistics and Computer Science University of Chicago 5735 S Ave Chicago, IL 60637, USA |
| Pseudocode | Yes | Algorithm 1 Generic Bagging input Base algorithm A; data set D with n training points; number of bags B ≥ 1; resampling distribution Qn... Algorithm 2 Derandomized Bagging input Base algorithm A; data set D with n training points; resampling distribution Qn... Algorithm 3 Adaptively Clipped Bagging input Base algorithm A; data set D with n training points; number of bags B ≥ 1; resampling distribution Qn; data-dependent range I( ) |
| Open Source Code | Yes | Code to reproduce all experiments is available at https://github.com/jake-soloff/subbagging-experiments. |
| Open Datasets | No | We simulate from the following data generating process: Xi ∼ iid N(0, Id), Yi | Xi ∼ ind Bernoulli 1 1 + exp −XiTθ with sample size n = 500 and dimension d = 200, and where θ = (.1, . . . , .1) ∈ Rd. |
| Dataset Splits | No | The paper describes generating data for simulations with specific sample sizes (e.g., n = 500) and how test points (Xn+1) are generated from the same distribution, but does not describe splitting a larger pre-existing dataset into training, validation, or test sets. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware (e.g., GPU, CPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | We use scikitlearn (Pedregosa et al., 2011) for all base algorithms. We use sklearn.linear_model.Logistic Regression, ... sklearn.neural_network.MLPClassifier, ... sklearn.tree.Decision Tree Regressor. |
| Experiment Setup | Yes | In each setting, we apply the base algorithm A as well as subbagging e AB with m = n/2 samples in each bag, using B = 10000 bags. ... We use sklearn.linear_model.Logistic Regression, setting options penalty='l2', C=1e3/n and fit_intercept=False, leaving all other parameters at their default values. ... We use sklearn.neural_network.MLPClassifier, setting hidden_layer_sizes=(40,), solver="sgd", learning_rate_init=0.2, max_iter=8, and alpha=1e-4, leaving all other parameters at their default values. ... We apply sklearn.tree.Decision Tree Regressor to train the regression trees, setting max_depth=50 and leaving all other parameters at their default values. |