Ivan Petej, Vladimir Vovk
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 |
|---|---|---|
| ip200/ivar-experiments | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| get_tex_table | Ran | ip200/ivar-experiments/src/generate_tables.py code served (permissive licence) · get_code("4d3add977490965a") |
| get_tex_table | Ran | ip200/ivar-experiments/src/generate_tables_local.py code served (permissive licence) · get_code("1f1ab14acbe682e5") |
| run_job | Ran | ip200/ivar-experiments/src/parallel_run.py code served (permissive licence) · get_code("cbaf567be54d6cbd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Venn-Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they are applicable only to the case of binary classification, with a recent extension to bounded regression. We generalize them to the case of unbounded regression, which requires adding an element of conformal prediction. In our simulation and empirical studies we investigate the predictive efficiency of point regressors derived from Venn-Abers regressors and argue that they somewhat improve the predictive efficiency of standard regressors for larger training sets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2605.06646")
get_code_for_paper("2605.06646")
have("2605.06646")
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