Automated Tuning of Closed-loop Neuromodulation Control Systems using Bayesian Optimization. - Parisa Sarikhani; Hao-Lun Hsu; Babak Mahmoudi (EMBC, 2022)

Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning. - Hao-Lun Hsu; Qiuhua Huang; Sehoon Ha (ICRA, 2022)

Robust Reinforcement Learning through Efficient Adversarial Herding. - Juncheng Dong; Hao-Lun Hsu; Qitong Gao; Vahid Tarokh; Miroslav Pajic (CoRR, 2023)

Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. - Tianyuan Jin; Hao-Lun Hsu; William Chang; Pan Xu 002 (AAAI, 2024)

StressFADS: Learning Latent Autonomic Factors of Stress in the Context of Trauma Recall and Neuromodulation. - Asim Hossain Gazi; Michael Chan 006; Hao-Lun Hsu; J. Douglas Bremner; Christopher J. Rozell; Omer T. Inan (BSN, 2024)

ϵ-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment. - Hao-Lun Hsu; Qitong Gao; Miroslav Pajic (ICCPS, 2024)

REFORMA: Robust REinFORceMent Learning via Adaptive Adversary for Drones Flying under Disturbances. - Hao-Lun Hsu; Haocheng Meng; Shaocheng Luo; Juncheng Dong; Vahid Tarokh; Miroslav Pajic (ICRA, 2024)

Steering Decision Transformers via Temporal Difference Learning. - Hao-Lun Hsu; Alper Kamil Bozkurt; Juncheng Dong; Qitong Gao; Vahid Tarokh; Miroslav Pajic (IROS, 2024)

Robust exploration with adversary via Langevin Monte Carlo. - Hao-Lun Hsu; Miroslav Pajic (L4DC, 2024)

Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. - Hao-Lun Hsu; Weixin Wang; Miroslav Pajic; Pan Xu 002 (NeurIPS, 2024)

Variational Adversarial Training Towards Policies with Improved Robustness. - Juncheng Dong; Hao-Lun Hsu; Qitong Gao; Vahid Tarokh; Miroslav Pajic (AISTATS, 2025)

Safe Cooperative Multi-Agent Reinforcement Learning with Function Approximation. - Hao-Lun Hsu; Miroslav Pajic (L4DC, 2025)