Hao-Lun Hsu

Conference · October 15, 2024 Physiological markers of stress and neuromodulation (e.g., heart rate variability) are often inconsistent when it comes to quantifying changes in autonomic nervous system function. This inconsistency is explained by the autonomic nervous system's output va ... Full text Link to item Cite

Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs

Conference Proceedings of the Aaai Conference on Artificial Intelligence · March 25, 2024 We study the multi-agent multi-armed bandit (MAMAB) problem, where agents are factored into overlapping groups. Each group represents a hyperedge, forming a hypergraph over the agents. At each round of interaction, the learner pulls a joint arm (composed o ... Full text Cite

ϵ-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment

Conference Proceedings 15th ACM IEEE International Conference on Cyber Physical Systems Iccps 2024 · January 1, 2024 Deep Brain Stimulation (DBS) stands as an effective intervention for alleviating the motor symptoms of Parkinson's disease (PD). Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions o ... Full text Cite

REFORMA: Robust REinFORceMent Learning via Adaptive Adversary for Drones Flying under Disturbances

Conference Proceedings IEEE International Conference on Robotics and Automation · January 1, 2024 In this work, we introduce REFORMA, a novel robust reinforcement learning (RL) approach to design controllers for unmanned aerial vehicles (UAVs) robust to unknown disturbances during flights. These disturbances, typically due to wind turbulence, electroma ... Full text Cite

Robust Exploration with Adversary via Langevin Monte Carlo

Conference Proceedings of Machine Learning Research · January 1, 2024 In the realm of Deep Q-Networks (DQNs), numerous exploration strategies have demonstrated efficacy within controlled environments. However, these methods encounter formidable challenges when confronted with the unpredictability of real-world scenarios mark ... Cite

Steering Decision Transformers via Temporal Difference Learning

Conference IEEE International Conference on Intelligent Robots and Systems · January 1, 2024 Decision Transformers (DTs) have been highly effective for offline reinforcement learning (RL) tasks, successfully modeling the sequences of actions in a given set of demonstrations. However, DTs may perform poorly in stochastic environments, which are pre ... Full text Cite