California Institute of Technology

Daniel Golovin

Debajyoti Ray

Andreas Krause

Colin Camerer

Systems and methods for active learning decision engines in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, an active learning decision engine includes equivalence class storage, hypotheses storage, edge storage, test storage, where tests are related to hypotheses, observation storage; and a processor, where the processor is configured to determine a plurality of equivalence classes containing one or more hypotheses, determine a set of edges utilizing tests, where the edges in the set of edges span hypotheses in distinct equivalence classes, determine weights for the determined edges, select a test based on the determined weights, perform the selected test and observe the results of the performed test, remove edges from the set of edges utilizing the observed results, and select a hypothesis from the one or more hypotheses using the set of edges.

Growing community of inventors

Pittsburgh, PA, United States of America

Daniel Golovin