16 citations · 17 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 1 cited
Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability
Timo Bertram, Johannes Fürnkranz, Martin Müller
In this paper, we study learning in probabilistic domains where the learner may receive incorrect labels but can improve the reliability of labels by repeatedly sampling them. In s…
cs.LG2019★ 16 cited
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Chenjun Xiao, Yifan Wu, Chen Ma +2
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorit…