We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jake-soloff/subbagging-experiments | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| fit_bags | Ran | jake-soloff/subbagging-experiments/stability-experiments.py pointer only (licence: NONE) · get_code("7c820593e113bb01") |
| sig | Ran | jake-soloff/subbagging-experiments/stability-experiments.py pointer only (licence: NONE) · get_code("847b26af2feb8c96") |
| subsample | Ran | jake-soloff/subbagging-experiments/stability-experiments.py pointer only (licence: NONE) · get_code("15530dfaee2fe87d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Bagging is an important technique for stabilizing machine learning models. In this paper, we derive a finite-sample guarantee on the stability of bagging for any model. Our result places no assumptions on the distribution of the data, on the properties of the base algorithm, or on the dimensionality of the covariates. Our guarantee applies to many variants of bagging and is optimal up to a constant. Empirical results validate our findings, showing that bagging successfully stabilizes even highly unstable base algorithms.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2301.12600")
get_code_for_paper("2301.12600")
have("2301.12600")
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