194 citations · 214 across the 8 of their papers we have counts for
6 papers · 1 filter
Safe and Robust Reinforcement Learning: Principles and Practice
Taku Yamagata, Raul Santos-Rodriguez
Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges…
When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations
Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez +5
In supervised learning, low quality annotations lead to poorly performing classification and detection models, while also rendering evaluation unreliable. This is particularly appa…
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
Taku Yamagata, Ahmed Khalil, Raul Santos-Rodriguez
Recent works have shown that tackling offline reinforcement learning (RL) with a conditional policy produces promising results. The Decision Transformer (DT) combines the condition…
The Weak Supervision Landscape
Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesus Cid-Sueiro +3
Many ways of annotating a dataset for machine learning classification tasks that go beyond the usual class labels exist in practice. These are of interest as they can simplify or f…
Classifier Calibration: A survey on how to assess and improve predicted class probabilities
Telmo Silva Filho, Hao Song, Miquel Perello-Nieto +3
This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the…
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills
Taku Yamagata, Ryan McConville, Raul Santos-Rodriguez
A promising approach to improve the robustness and exploration in Reinforcement Learning is collecting human feedback and that way incorporating prior knowledge of the target envir…