Quick Summary: Abstract: Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions. Abstract: Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form ...
Machine Learning Needs Mathematical Optimization With Prof Stan Uryasev -
Abstract: Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions. Abstract: Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form ... This work develops a class of relaxations in between the big-M and convex hull formulations of disjunctions, drawing ...
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- Abstract: Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions.
- Abstract: Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form ...
- This work develops a class of relaxations in between the big-M and convex hull formulations of disjunctions, drawing ...
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