Gradient boosting with vector-valued leafs
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 grad
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- PossiblePossibly related (embedding) · 28%lightgbm-org/LightGBM →
“Possibly related via embedding similarity 0.56 (not asserted). Timestamp check: artifact after paper (+5d).”
- PossiblePossibly related (embedding) · 29%catboost/catboost →
“Possibly related via embedding similarity 0.58 (not asserted). Timestamp check: artifact after paper (+4d).”
- PossiblePossibly related (embedding) · 56%fabsig/GPBoost →
- PossiblePossibly related (embedding) · 49%mlpack/mlpack →
- PossiblePossibly related (embedding) · 56%google/yggdrasil-decision-forests →
- PossiblePossibly related (embedding) · 45%jaywyawhare/C-ML →
- PossiblePossibly related (embedding) · 51%dmlc/xgboost →