Algorithms for identifying and learning under conditional distributions
Abstract
This proposal studies fundamental questions about search problems formulated by data distributions and incomplete information or background knowledge using a probabilistic semantics, known as PAC-semantics, for logical presentations. This data-driven formalism captures inductive generalization as opposed to deduction, to allow reasoning in a partial information model. For instance, a key algorithmic problem in PAC-Semantics is to certify a logical formula as "almost" valid on the basis of examples drawn from the background distribution. This approach to learning and reasoning with incomplete information on data is very different from other logical, statistical, or probabilistic reasoning models.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 23, 2016
- Source ID
- FA95501510209
Entities
People
- Brendan Juba
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- Washington University in St. Louis