5 papers
Causal Flow Q-Learning for Robust Offline Reinforcement Learning
Mingxuan Li, Junzhe Zhang, Elias Bareinboim
Expressive policies based on flow-matching have been successfully applied in reinforcement learning (RL) more recently due to their ability to model complex action distributions fr…
Confounding Robust Deep Reinforcement Learning: A Causal Approach
Mingxuan Li, Junzhe Zhang, Elias Bareinboim
A key task in Artificial Intelligence is learning effective policies for controlling agents in unknown environments to optimize performance measures. Off-policy learning methods, l…
Automatic Reward Shaping from Confounded Offline Data
Mingxuan Li, Junzhe Zhang, Elias Bareinboim
A key task in Artificial Intelligence is learning effective policies for controlling agents in unknown environments to optimize performance measures. Off-policy learning methods, l…
Causally Aligned Curriculum Learning
Mingxuan Li, Junzhe Zhang, Elias Bareinboim
A pervasive challenge in Reinforcement Learning (RL) is the "curse of dimensionality" which is the exponential growth in the state-action space when optimizing a high-dimensional t…
Measuring Fairness in Financial Transaction Machine Learning Models
Deniz Sezin Ayvaz, Lorenzo Belenguer, Hankun He +12
Mastercard, a global leader in financial services, develops and deploys machine learning models aimed at optimizing card usage and preventing attrition through advanced predictive…