Zobrazeno 1 - 10
of 442
pro vyhledávání: '"Leong, Tze Yun"'
Reward shaping is effective in addressing the sparse-reward challenge in reinforcement learning by providing immediate feedback through auxiliary informative rewards. Based on the reward shaping strategy, we propose a novel multi-task reinforcement l
Externí odkaz:
http://arxiv.org/abs/2408.10858
Reward shaping is a technique in reinforcement learning that addresses the sparse-reward problem by providing more frequent and informative rewards. We introduce a self-adaptive and highly efficient reward shaping mechanism that incorporates success
Externí odkaz:
http://arxiv.org/abs/2408.03029
Action recognition technology plays a vital role in enhancing security through surveillance systems, enabling better patient monitoring in healthcare, providing in-depth performance analysis in sports, and facilitating seamless human-AI collaboration
Externí odkaz:
http://arxiv.org/abs/2407.14811
Decentralized data sources are prevalent in real-world applications, posing a formidable challenge for causal inference. These sources cannot be consolidated into a single entity owing to privacy constraints. The presence of dissimilar data distribut
Externí odkaz:
http://arxiv.org/abs/2308.13047
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random
Externí odkaz:
http://arxiv.org/abs/2301.00346
Autor:
Li, Xiaoli, Leong, Tze Yun
We propose an information extraction framework to support automated construction of decision models in biomedicine. Our proposed technique classifies text-based documents from a large biomedical literature repository, e.g., MEDLINE, into predefined c
Externí odkaz:
http://hdl.handle.net/1721.1/3852
Autor:
Leong, Tze Yun
How do we make the best decisions in face of voluminous, complex, changing, and uncertain information? We describe a multi-disciplinary effort in developing the next generation decision analytic and engineering technologies. We explain the goals, the
Externí odkaz:
http://hdl.handle.net/1721.1/3762
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed. Till date, most insight into the causal inference requires data to be stored in a central repository. We present a novel framework for causa
Externí odkaz:
http://arxiv.org/abs/2106.00456
Data scarcity is a tremendous challenge in causal effect estimation. In this paper, we propose to exploit additional data sources to facilitate estimating causal effects in the target population. Specifically, we leverage additional source datasets w
Externí odkaz:
http://arxiv.org/abs/2105.14877
This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz e
Externí odkaz:
http://arxiv.org/abs/2008.03444