Introduction
Continuous approximation
Structured output learning
Perspectives
The objective function can be much more general: R(f (x1 ), . . . , f (xn )) gj = − ∂f∂R (xj ) . For ranking via structured output learning: R(f ) =
X q
max ∆(y , yq ) + y
Q X
! f (xqi )(A(yi ) − A(yqi )) .
i=1
Preliminary results are disappointing: with gradient boosted decision trees, no difference between regression and structured output learning. −→ Could be because the loss function matters only when the class of functions is restricted (underfitting).