5 papers
Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach
Bo Liu, Shao-Bo Lin, Changmiao Wang +1
Time series forecasting enables early warning and has driven asset performance management from traditional planned maintenance to predictive maintenance. However, the lack of inter…
Two-Stage Data Synthesization: A Statistics-Driven Restricted Trade-off between Privacy and Prediction
Xiaotong Liu, Shao-Bo Lin, Jun Fan +1
Synthetic data have gained increasing attention across various domains, with a growing emphasis on their performance in downstream prediction tasks. However, most existing synthesi…
Non-Rival Data as Rival Products: An Encapsulation-Forging Approach for Data Synthesis
Kaidong Wang, Jiale Li, Shao-Bo Lin +1
The non-rival nature of data creates a dilemma for firms: sharing data unlocks value but risks eroding competitive advantage. Existing data synthesis methods often exacerbate this…
Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approach
Qianxin Yi, Shao-Bo Lin, Jun Fan +1
Reinforcement learning (RL) has been widely applied to sequential decision making, where interpretability and performance are both critical for practical adoption. Current approach…
Striking the Perfect Balance: Preserving Privacy While Boosting Utility in Collaborative Medical Prediction Platforms
Shao-Bo Lin, Xiaotong Liu, Yao Wang
Online collaborative medical prediction platforms offer convenience and real-time feedback by leveraging massive electronic health records. However, growing concerns about privacy…