Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms

no code implementations • ICML 2018 • Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell

Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements.

Probabilistic Programming

Algorithm selection by rational metareasoning as a model of human strategy selection

no code implementations • NeurIPS 2014 • Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Tom Griffiths

Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.

Selecting Computations: Theory and Applications

no code implementations • 9 Aug 2014 • Nicholas Hay, Stuart Russell, David Tolpin, Solomon Eyal Shimony

Sequential decision problems are often approximately solvable by simulating possible future action sequences.

Game of Go