5 citations · 12 across the 6 of their papers we have counts for
6 papers
Optimising Human-AI Collaboration by Learning Convincing Explanations
Alex J. Chan, Alihan Huyuk, Mihaela van der Schaar
Machine learning models are being increasingly deployed to take, or assist in taking, complicated and high-impact decisions, from quasi-autonomous vehicles to clinical decision sup…
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar
Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeli…
Online Decision Mediation
Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar
Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior: At each time, the algorithm observes an…
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar
Decision analysis deals with modeling and enhancing decision processes. A principal challenge in improving behavior is in obtaining a transparent description of existing behavior i…
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples
Hao Sun, Alihan Hüyük, Daniel Jarrett +1
Learning controllers with offline data in decision-making systems is an essential area of research due to its potential to reduce the risk of applications in real-world systems. Ho…
Neural Laplace Control for Continuous-time Delayed Systems
Samuel Holt, Alihan Hüyük, Zhaozhi Qian +2
Many real-world offline reinforcement learning (RL) problems involve continuous-time environments with delays. Such environments are characterized by two distinctive features: firs…