18 citations · 26 across the 10 of their papers we have counts for
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math.ST2021★ 1 cited
Efficient Bayesian network structure learning via local Markov boundary search
Ming Gao, Bryon Aragam
We analyze the complexity of learning directed acyclic graphical models from observational data in general settings without specific distributional assumptions. Our approach is inf…
cs.LG2021★ 3 cited
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Goutham Rajendran, Bohdan Kivva, Ming Gao +1
Greedy algorithms have long been a workhorse for learning graphical models, and more broadly for learning statistical models with sparse structure. In the context of learning direc…