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Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency
Yuxiang Luo, Andrew Perrault
Recovering continuous-time dynamics from discrete observations is difficult because local supervision loses fidelity as the observation interval grows. We replace local supervision…
Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning
Xueqiao Peng, Andrew Perrault
Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are often constrained by limited re…
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution
Adam Żychowski, Andrew Perrault, Jacek Mańdziuk
Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The p…
The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards
Xi Chen, Zhihui Zhu, Andrew Perrault
The reward signal plays a central role in defining the desired behaviors of agents in reinforcement learning (RL). Rewards collected from realistic environments could be perturbed,…
Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret
Adam Żychowski, Andrew Perrault, Jacek Mańdziuk
In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted,…