  We present an adaptive algorithm with one-sided error for the problem of
junta testing for Boolean function under the challenging distribution-free
setting, the query complexity of which is $\widetilde O(k)/\epsilon$. This
improves the upper bound of $\widetilde O(k^2)/\epsilon$ by
\cite{liu2019distribution}. From the $\Omega(k\log k)$ lower bound for junta
testing under the uniform distribution by \cite{sauglam2018near}, our algorithm
is nearly optimal. In the standard uniform distribution, the optimal junta
testing algorithm is mainly designed by bridging between relevant variables and
relevant blocks. At the heart of the analysis is the Efron-Stein orthogonal
decomposition. However, it is not clear how to generalize this tool to the
general setting. Surprisingly, we find that junta could be tested in a very
simple and efficient way even in the distribution-free setting. It is
interesting that the analysis does not rely on Fourier tools directly which are
commonly used in junta testing.
