Tsinghua team turns the SIS proposal into a learning problem with GFlowNets: one network zero-shot matches or beats the post-hoc best of 31 analytic proposals on 1,190 unseen margins
The work shows that the zero-variance sequential importance sampling (SIS) proposal for binary matrices with fixed margins is exactly the policy of a unit-reward GFlowNet, and proposes MarginFlow, a set transformer that reads the remaining margins and, trained on 1,904 margins, runs zero-shot on 1,190 held-out margins, matching or beating the post-hoc best of 31 analytically designed configurations on 1,187 of them with a median effective sample fraction of 99.8%.