32 citations · 106 across the 33 of their papers we have counts for
4 papers · 1 filter
Generating Likely Counterfactuals Using Sum-Product Networks
Jiri Nemecek, Tomas Pevny, Jakub Marecek
The need to explain decisions made by AI systems is driven by both recent regulation and user demand. The decisions are often explainable only post hoc. In counterfactual explanati…
Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
Leah Chrestien, Tomás Pevný, Stefan Edelkamp +1
In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient condi…
Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning
Leah Chrestien, Tomas Pevny, Antonin Komenda +1
Learning a well-informed heuristic function for hard task planning domains is an elusive problem. Although there are known neural network architectures to represent such heuristic…
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…