David Cortes
We lifted 5 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| dmlc/xgboost | canonical | 4 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| c_str | Ran | dmlc/xgboost/python-package/xgboost/_c_api.py code served (permissive licence) · get_code("0d8f0cd07c110959") |
| from_pystr_to_cstr | Ran | dmlc/xgboost/python-package/xgboost/_c_api.py code served (permissive licence) · get_code("87d692e5415117a7") |
| lazy_isinstance | Ran | dmlc/xgboost/python-package/xgboost/compat.py code served (permissive licence) · get_code("db21a10670081f9e") |
| py_str | Ran | dmlc/xgboost/python-package/xgboost/compat.py code served (permissive licence) · get_code("fea37025885dd1ff") |
| allreduce | Not yet run | dmlc/xgboost/python-package/xgboost/collective.py code served (permissive licence) · get_code("7a4cfe12cf611fc1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Gradient boosting in the form of decision tree ensembles has successfully been applied to a variety of problems using simple objective functions based on log-likelihoods of a single variable. The concept extends naturally to objective functions operating on vectors -for example, multinomial logistic log-likelihood for multi-class classification, where observations have a score for each class -but popular frameworks approach these functions by either updating one value of the input vectors at a time, or by using a diagonal upper bound on the second derivative. This work extends the usual gradient boosting framework to functions of vector inputs and sketches a simple algorithm that can be used efficiently with histogram-based decision trees.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2606.29326")
get_code_for_paper("2606.29326")
have("2606.29326")
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