Search Results for author:
Found 6 papers, 3 papers with code
Synthesis of Hierarchical Controllers Based on Deep Reinforcement Learning Policies
We propose a novel approach to the problem of controller design for environments modeled as Markov decision processes (MDPs).
Into the Unknown: Active Monitoring of Neural Networks
To address this challenge, we introduce an algorithmic framework for active monitoring of a neural network.
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy.
Formal Methods with a Touch of Magic
Machine learning and formal methods have complimentary benefits and drawbacks.
Outside the Box: Abstraction-Based Monitoring of Neural Networks
Neural networks have demonstrated unmatched performance in a range of classification tasks.
ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans
Inspired by Importance Splitting, the length of the horizon and the number of particles are chosen such that at least one particle reaches a next-level state, that is, a state where the cost decreases by a required delta from the previous-level state.
