Mon 17 Jan 2022 13:30 - 14:00 at Salon I - Formal Methods in Machine Learning Chair(s): Rupak Majumdar

Machine learning becomes increasingly important to control the behavior of safety and financially critical components in sophisticated environments, where the inability to understand learned components in general, and neural nets in particular, poses serious obstacles to their adoption. Explainability and interpretability methods for learned systems have gained considerable academic attention, but the focus of current approaches on only one aspect of explanation, at a fixed level of abstraction, and limited if any formal guarantees, prevents those explanations from being digestible by the relevant stakeholders (e.g., end users, certification authorities, engineers) with their diverse backgrounds and situation-specific needs. We introduce Fanoos, a framework for combining formal verification techniques, heuristic search, and user interaction to explore explanations at the desired level of granularity and fidelity. We demonstrate the ability of Fanoos to produce and adjust the abstractness of explanations in response to user requests on a learned controller for an inverted double pendulum and on a learned CPU usage model.

Mon 17 Jan

Displayed time zone: Eastern Time (US & Canada) change

13:30 - 14:30
Formal Methods in Machine LearningVMCAI at Salon I
Chair(s): Rupak Majumdar MPI-SWS
13:30
30m
Paper
Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned SystemsRemote
VMCAI
David Bayani Carnegie Mellon University's School of Computer Science, Stefan Mitsch Carnegie Mellon University, USA
14:00
30m
Paper
Bisimulations for Neural Network ReductionInPerson
VMCAI
Pavithra Prabhakar Kansas State University