5 citations · 31 across the 19 of their papers we have counts for
11 papers · 1 filter
Learnability with PAC Semantics for Multi-agent Beliefs
Ionela G. Mocanu, Vaishak Belle, Brendan Juba
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. In an influential paper, Valia…
An Example of the SAM+ Algorithm for Learning Action Models for Stochastic Worlds
Brendan Juba, Roni Stern
In this technical report, we provide a complete example of running the SAM+ algorithm, an algorithm for learning stochastic planning action models, on a simplified PPDDL version of…
Safe Learning of Lifted Action Models
Brendan Juba, Hai S. Le, Roni Stern
Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a d…
Probabilistic Generating Circuits
Honghua Zhang, Brendan Juba, Guy Van den Broeck
Generating functions, which are widely used in combinatorics and probability theory, encode function values into the coefficients of a polynomial. In this paper, we explore their u…
Learning Implicitly with Noisy Data in Linear Arithmetic
Alexander P. Rader, Ionela G. Mocanu, Vaishak Belle +1
Robust learning in expressive languages with real-world data continues to be a challenging task. Numerous conventional methods appeal to heuristics without any assurances of robust…
Query-driven PAC-Learning for Reasoning
Brendan Juba
We consider the problem of learning rules from a data set that support a proof of a given query, under Valiant's PAC-Semantics. We show how any backward proof search algorithm that…