1 code implementation • 28 May 2023 • Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam
Then we proposed a robust and computationally-efficient method modified from MLE that does not rely on the prior estimation of the heterogeneous intensity and is thus applicable in a data-limited regime (e. g., few-shot, no repeated observations).
1 code implementation • 12 May 2023 • Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka
We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.
no code implementations • 26 May 2022 • Fengpei Li, Vitalii Ihnatiuk, Ryan Kinnear, Anderson Schneider, Yuriy Nevmyvaka
Market impact is an important problem faced by large institutional investor and active market participant.
1 code implementation • 19 Jun 2021 • Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.
no code implementations • 14 Oct 2019 • Henry Lam, Fengpei Li, Siddharth Prusty
In many learning problems, the training and testing data follow different distributions and a particularly common situation is the \textit{covariate shift}.