29 citations · 34 across the 4 of their papers we have counts for
5 papers
NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios
Jaromír Janisch, Tomáš Pevný, Viliam Lisý
Current frameworks for training offensive penetration testing agents with deep reinforcement learning struggle to produce agents that perform well in real-world scenarios, due to t…
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy Decomposition
Jaromír Janisch, Tomáš Pevný, Viliam Lisý
We focus on reinforcement learning (RL) in relational problems that are naturally defined in terms of objects, their relations, and object-centric actions. These problems are chara…
Classification with Costly Features in Hierarchical Deep Sets
Jaromír Janisch, Tomáš Pevný, Viliam Lisý
Classification with Costly Features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features a…
Classification with Costly Features as a Sequential Decision-Making Problem
Jaromír Janisch, Tomáš Pevný, Viliam Lisý
This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample…
Classification with Costly Features using Deep Reinforcement Learning
Jaromír Janisch, Tomáš Pevný, Viliam Lisý
We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature co…