A5009357142- Stock Market Forecasting Methods
- Financial Markets and Investment Strategies
- Complex Systems and Time Series Analysis
- Private Equity and Venture Capital
- Reinforcement Learning in Robotics
- Advanced Bandit Algorithms Research
- Data Quality and Management
- Advanced Database Systems and Queries
- Semantic Web and Ontologies
- Cancer-related molecular mechanisms research
- RNA and protein synthesis mechanisms
- RNA modifications and cancer
Nanyang Technological University
2024
Xi’an Jiaotong-Liverpool University
2021-2022
Abstract Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all types. Precise identification of modification sites essential understanding the functions and regulatory mechanisms RNAs. Here, we present MultiRM, a method integrated prediction interpretation from sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts putative twelve widely occurring transcriptome (m 6...
Portfolio management (PM) is a fundamental financial trading task, which explores the optimal periodical reallocation of capitals into different stocks to pursue long-term profits. Reinforcement learning (RL) has recently shown its potential train profitable agents for PM through interacting with markets. However, existing work mostly focuses on fixed stock pools, inconsistent investors' practical demand. Specifically, target pool investors varies dramatically due their discrepancy market...
Modern high-stakes systems, such as healthcare or robotics, often generate vast streaming event sequences. Our goal is to design an efficient, plug-and-play tool elicit logic tree-based explanations from Large Language Models (LLMs) provide customized insights into each observed sequence. Built on the temporal point process model for events, our method employs likelihood function a score evaluate generated trees. We propose amortized Expectation-Maximization (EM) learning framework and treat...
Portfolio management (PM) is a fundamental financial trading task, which explores the optimal periodical reallocation of capitals into different stocks to pursue long-term profits. Reinforcement learning (RL) has recently shown its potential train profitable agents for PM through interacting with markets. However, existing work mostly focuses on fixed stock pools, inconsistent investors' practical demand. Specifically, target pool investors varies dramatically due their discrepancy market...
In recent years, many practitioners in quantitative finance have attempted to use Deep Reinforcement Learning (DRL) build better trading (QT) strategies. Nevertheless, existing studies fail address several serious challenges, such as the non-stationary financial environment and bias variance trade-off when applying DRL real market. this work, we proposed Safe-FinRL, a novel DRL-based high-freq stock strategy enhanced by near-stationary low estimation. Our main contributions are twofold:...