We lifted 2 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| anaderi/lhcb_trigger_ml | canonical | 0 of 1 |
| thomaskeck/FastBDT | reimplementation | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| calculate_roc_auc | Not yet run | thomaskeck/FastBDT/PyFastBDT/FastBDT.py pointer only (licence: GPL-3.0) · get_code("c7adbd9383d373ef") |
| get_higgs_data | Not yet run | anaderi/lhcb_trigger_ml/hep_ml/experiments/gradient_boosting.py code served (permissive licence) · get_code("b7638c485831bd46") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The use of multivariate classifiers has become commonplace in particle physics. To enhance the performance, a series of classifiers is typically trained; this is a technique known as boosting. This paper explores several novel boosting methods that have been designed to produce a uniform selection efficiency in a chosen multivariate space. Such algorithms have a wide range of applications in particle physics, from producing uniform signal selection efficiency across a Dalitz-plot to avoiding the creation of false signal peaks in an invariant mass distribution when searching for new particles.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1410.4140")
get_code_for_paper("1410.4140")
have("1410.4140")
Connect an agent — have() is free.