We propose a conservative extension of HM(X), a generic constraint-based type inference framework, with bounded existential (a.k.a. abstract) and universal (a.k.a. polymorphic) da...
We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. ...
In 2003, Hofmann and Jost introduced a type system that uses a potential-based amortized analysis to infer bounds on the resource consumption of (first-order) functional programs. ...
Previous work on context-specific independence in Bayesian networks is driven by a common goal, namely to represent the conditional probability tables in a most compact way. In th...
Abstract. We present a generic framework for the automatic and modular inference of sound class invariants for class-based object oriented languages. The idea is to derive a sound ...