9 citations · 14 across the 6 of their papers we have counts for
6 papers
Variance Reduction Based Experience Replay for Policy Optimization
Hua Zheng, Wei Xie, M. Ben Feng +1
Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and accelerate policy optimization. However, c…
Efficient Input Uncertainty Quantification for Ratio Estimator
Linyun He, Ben Feng, Eunhye Song
We study the construction of a confidence interval (CI) for a simulation output performance measure that accounts for input uncertainty when the input models are estimated from fin…
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection
Katrina Chen, Mingbin Feng, Tony S. Wirjanto
Time series anomaly detection (TSAD) plays a vital role in many industrial applications. While contrastive learning has gained momentum in the time series domain for its prowess in…
Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting
Katrina Chen, Mingbin Feng, Tony S. Wirjanto
Anomalies in univariate time series often refer to abnormal values and deviations from the temporal patterns from majority of historical observations. In multivariate time series,…
On the Convergence of Experience Replay in Policy Optimization: Characterizing Bias, Variance, and Finite-Time Convergence
Hua Zheng, Wei Xie, M. Ben Feng
Experience replay is a core ingredient of modern deep reinforcement learning, yet its benefits in policy optimization are poorly understood beyond empirical heuristics. This paper…
Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Control
Hua Zheng, Wei Xie, Mingbin Ben Feng
Biopharmaceutical manufacturing faces critical challenges, including complexity, high variability, lengthy lead time, and limited historical data and knowledge of the underlying sy…