3 citations · 6 across the 4 of their papers we have counts for
4 papers
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning
Dapeng Li, Feiyang Pan, Jia He +3
In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable. For example, vo…
Beyond Moments: Robustly Learning Affine Transformations with Asymptotically Optimal Error
He Jia, Pravesh K . Kothari, Santosh S. Vempala
We present a polynomial-time algorithm for robustly learning an unknown affine transformation of the standard hypercube from samples, an important and well-studied setting for inde…
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution
Feiyang Pan, Tongzhe Zhang, Ling Luo +2
Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-spli…
In-Network Processing for Low-Latency Industrial Anomaly Detection in Softwarized Networks
Huanzhuo Wu, Jia He, Máté Tömösközi +2
Modern manufacturers are currently undertaking the integration of novel digital technologies - such as 5G-based wireless networks, the Internet of Things (IoT), and cloud computing…