2 citations · 5 across the 5 of their papers we have counts for
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cs.LG2023★ 1 cited
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks
Yeshu Li, Brian D. Ziebart
We consider the problem of learning the exact skeleton of general discrete Bayesian networks from potentially corrupted data. Building on distributionally robust optimization and a…
cs.LG2023
Superhuman Fairness
Omid Memarrast, Linh Vu, Brian Ziebart
The fairness of machine learning-based decisions has become an increasingly important focus in the design of supervised machine learning methods. Most fairness approaches optimize…
cs.LG2012★ 2 cited
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification
Brian D. Ziebart, Anind K. Dey, J Andrew Bagnell
Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponenti…