  Revealing the Higgs pair production process is the next big challenge in high
energy physics. In this work, we explore the use of interpretable machine
learning and cooperative game theory for extraction of the trilinear Higgs
self-coupling in Higgs pair production. In particular, we show how a
topological decomposition of the gluon-gluon fusion Higgs pair production
process can be used to simplify the machine learning analysis flow.
Furthermore, we extend the analysis to include $q\bar{q}\to hh$ production,
which is strongly suppressed in the Standard Model, to extract the trilinear
Higgs coupling and to bound large deviations of the light-quark Yukawa
couplings from the Standard Model values. The constraints on the rescaling of
the trilinear Higgs self-coupling, $\kappa_\lambda$, and the rescaling of
light-quark Yukawa couplings, $\kappa_u$ and $\kappa_d$, at HL-LHC (FCC-hh)
from single parameter fits are: \begin{eqnarray}
  \kappa_\lambda&=&[0.53,1.7] \;\;([0.97, 1.03])\nonumber \newline
  \kappa_u&=&[-470,430] \;\;([-58,55])\nonumber \newline
  \kappa_d&=&[-360,360] \;\;([-26,28])\nonumber \end{eqnarray} We show that the
simultaneous modification of the Yukawa couplings can dilute the constraints on
the trilinear coupling significantly. We perform similar analyses for FCC-hh.
We discuss some motivated flavourful new physics scenarios where such an
analysis prevails.
