21 citations · 31 across the 7 of their papers we have counts for
7 papers
Privacy-Aware Time Series Synthesis via Public Knowledge Distillation
Penghang Liu, Haibei Zhu, Eleonora Kreacic +1
Sharing sensitive time series data in domains such as finance, healthcare, and energy consumption, such as patient records or investment accounts, is often restricted due to privac…
Variational Neural Stochastic Differential Equations with Change Points
Yousef El-Laham, Zhongchang Sun, Haibei Zhu +2
In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training…
A Language Model-Guided Framework for Mining Time Series with Distributional Shifts
Haibei Zhu, Yousef El-Laham, Elizabeth Fons +1
Effective utilization of time series data is often constrained by the scarcity of data quantity that reflects complex dynamics, especially under the condition of distributional shi…
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection
Zhenglin Li, Haibei Zhu, Houze Liu +2
This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-201…
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…
Once Burned, Twice Shy? The Effect of Stock Market Bubbles on Traders that Learn by Experience
Haibei Zhu, Svitlana Vyetrenko, Serafin Grundl +3
We study how experience with asset price bubbles changes the trading strategies of reinforcement learning (RL) traders and ask whether the change in trading strategies helps to pre…
A Model-Based Synthetic Stock Price Time Series Generation Framework
Haibei Zhu, Svitlana Vyetrenko, Tucker Balch
The Ornstein-Uhlenbeck (OU) process, a mean-reverting stochastic process, has been widely applied as a time series model in various domains. This paper describes the design and imp…