56 citations · 94 across the 9 of their papers we have counts for
6 papers · 1 filter
Q-based Variational Inverse Reinforcement Learning
Ondrej Bajgar, Peter Tisnikar, Alessandro Abate +2
The development of safe and beneficial AI requires that systems can learn and act in accordance with human preferences. However, explicitly specifying these preferences by hand is…
PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning
Ondrej Bajgar, Dewi S. W. Gould, Jonathon Liu +3
As AI systems become increasingly autonomous, reliably aligning their decision-making with human preferences is essential. Inverse reinforcement learning (IRL) offers a promising a…
Toward Information Theoretic Active Inverse Reinforcement Learning
Ondrej Bajgar, Sid William Gould, Rohan Narayan Langford Mitta +3
As AI systems become increasingly autonomous, aligning their decision-making to human preferences is essential. In domains like autonomous driving or robotics, it is impossible to…
Walking the Values in Bayesian Inverse Reinforcement Learning
Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis +1
The goal of Bayesian inverse reinforcement learning (IRL) is recovering a posterior distribution over reward functions using a set of demonstrations from an expert optimizing for a…
A Boo(n) for Evaluating Architecture Performance
Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst
We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects…
Knowledge Base Completion: Baselines Strike Back
Rudolf Kadlec, Ondrej Bajgar, Jan Kleindienst
Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on…