12 citations · 19 across the 5 of their papers we have counts for
5 papers
Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level
Chase Yakaboski, Eugene Santos
Successful machine learning methods require a trade-off between memorization and generalization. Too much memorization and the model cannot generalize to unobserved examples. Too m…
On the Generation of Alternative Explanations with Implications for Belief Revision
Eugene Santos
In general, the best explanation for a given observation makes no promises on how good it is with respect to other alternative explanations. A major deficiency of message-passing s…
Belief Updating by Enumerating High-Probability Independence-Based Assignments
Eugene Santos, Solomon Eyal Shimony
Independence-based (IB) assignments to Bayesian belief networks were originally proposed as abductive explanations. IB assignments assign fewer variables in abductive explanations…
Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks
Eugene Santos, Solomon Eyal Shimony, Edward Williams
Belief updating in Bayes nets, a well known computationally hard problem, has recently been approximated by several deterministic algorithms, and by various randomized approximatio…
Cost-Sharing in Bayesian Knowledge Bases
Solomon Eyal Shimony, Carmel Domshlak, Eugene Santos
Bayesian knowledge bases (BKBs) are a generalization of Bayes networks and weighted proof graphs (WAODAGs), that allow cycles in the causal graph. Reasoning in BKBs requires findin…