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Hao-Lun Hsu
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2020 – today
- 2024
- [c6]Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu:
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. AAAI 2024: 12956-12964 - [c5]Hao-Lun Hsu, Qitong Gao, Miroslav Pajic:
ϵ-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment. ICCPS 2024: 224-234 - [c4]Hao-Lun Hsu, Haocheng Meng, Shaocheng Luo, Juncheng Dong, Vahid Tarokh, Miroslav Pajic:
REFORMA: Robust REinFORceMent Learning via Adaptive Adversary for Drones Flying under Disturbances. ICRA 2024: 5169-5175 - [c3]Hao-Lun Hsu, Miroslav Pajic:
Robust exploration with adversary via Langevin Monte Carlo. L4DC 2024: 1592-1605 - [i5]Hao-Lun Hsu, Qitong Gao, Miroslav Pajic:
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment. CoRR abs/2403.06814 (2024) - [i4]Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu:
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2404.10728 (2024) - 2023
- [i3]Juncheng Dong, Hao-Lun Hsu, Qitong Gao, Vahid Tarokh, Miroslav Pajic:
Robust Reinforcement Learning through Efficient Adversarial Herding. CoRR abs/2306.07408 (2023) - [i2]Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu:
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. CoRR abs/2312.15549 (2023) - 2022
- [c2]Parisa Sarikhani, Hao-Lun Hsu, Babak Mahmoudi:
Automated Tuning of Closed-loop Neuromodulation Control Systems using Bayesian Optimization. EMBC 2022: 1734-1737 - [c1]Hao-Lun Hsu, Qiuhua Huang, Sehoon Ha:
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning. ICRA 2022: 5567-5573 - 2021
- [i1]Hao-Lun Hsu, Qiuhua Huang, Sehoon Ha:
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning. CoRR abs/2109.14325 (2021)
Coauthor Index
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last updated on 2024-08-20 22:49 CEST by the dblp team
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