4 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
Position: Ensuring mutual privacy is necessary for effective external evaluation of proprietary AI systems
Ben Bucknall, Robert F. Trager, Michael A. Osborne
The external evaluation of AI systems is increasingly recognised as a crucial approach for understanding their potential risks. However, facilitating external evaluation in practic…
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…
Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation
Lennart Heim, Tim Fist, Janet Egan +5
As jurisdictions around the world take their first steps toward regulating the most powerful AI systems, such as the EU AI Act and the US Executive Order 14110, there is a growing…