Explanation methods for machine learning models tend to not provide any
...
The difficulty of manually specifying reward functions has led to an int...
Compositional reinforcement learning is a promising approach for trainin...
Many future technologies rely on neural networks, but verifying the
corr...
Reinforcement learning has been shown to be an effective strategy for
au...
Debugging imperative network programs is a challenging task for develope...
We study the problem of learning control policies for complex tasks give...
Real-time data processing applications with low latency requirements hav...
We propose a novel hierarchical reinforcement learning framework for con...
Writing classification rules to identify malicious network traffic is a
...
Reinforcement learning is a promising approach for learning control poli...
The shortage of people trained in STEM fields is becoming acute, and
uni...
This paper describes a verification case study on an autonomous racing c...
Syntax-guided synthesis (SyGuS) is the computational problem of finding ...
Synthesis of finite-state controllers from high-level specifications in
...
Motivated by real-time monitoring and data processing applications, we
d...
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding ...