We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a co...
Christian R. Shelton, Yu Fan, William Lam, Joon Le...
We show that a wide class of bidirectional data-flow analyses and program optimizations based on them admit declarative descriptions in the form of type systems. The salient feat...
Concurrent programs are notorious for containing errors that are difficult to reproduce and diagnose at run-time. This motivated the development of type systems that statically en...
Amit Sasturkar, Rahul Agarwal, Liqiang Wang, Scott...
In languages that support polymorphic variants, a single variant value can be passed to many contexts that accept different sets of constructors. Polymorphic variants are potenti...
Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton...