169 citations · 799 across the 43 of their papers we have counts for
43 papers
A Practical Machine Learning Approach for Dynamic Stock Recommendation
Hongyang Yang, Xiao-Yang Liu, Qingwei Wu
Stock recommendation is vital to investment companies and investors. However, no single stock selection strategy will always win while analysts may not have enough time to check al…
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
Hongyang Yang, Xiao-Yang Liu, Shan Zhong +1
Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we pro…
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
Xiao-Yang Liu, Guoxuan Wang, Hongyang Yang +1
Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating human-like texts, which may potentially revolutionize the finance industry. Ho…
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Boyu Zhang, Hongyang Yang, Xiao-Yang Liu
Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive ca…
FinGPT: Open-Source Financial Large Language Models
Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang
Large language models (LLMs) have shown the potential of revolutionizing natural language processing tasks in diverse domains, sparking great interest in finance. Accessing high-qu…
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Hongyang Yang +6
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environmen…
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Jingyang Rui +6
Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement le…
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting
Berend Jelmer Dirk Gort, Xiao-Yang Liu, Xinghang Sun +3
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and opt…
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
Xiao-Yang Liu, Zechu Li, Zhuoran Yang +5
Deep reinforcement learning (DRL) has revolutionized learning and actuation in applications such as game playing and robotic control. The cost of data collection, i.e., generating…
Convolutional Graph-Tensor Net for Graph Data Completion
Xiao-Yang Liu, Ming Zhu
Graph data completion is a fundamentally important issue as data generally has a graph structure, e.g., social networks, recommendation systems, and the Internet of Things. We cons…
Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification
Fanjie Kong, Xiao-yang Liu, Ricardo Henao
Tiny object classification problem exists in many machine learning applications like medical imaging or remote sensing, where the object of interest usually occupies a small region…
Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation
Daizong Liu, Shuangjie Xu, Xiao-Yang Liu +3
This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these…
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
Xiang Fang, Yuchong Hu, Pan Zhou +1
Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored…
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
Xiang Fang, Yuchong Hu, Pan Zhou +1
Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.…
Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization
Daizong Liu, Xiaoye Qu, Xiao-Yang Liu +3
Query-based moment localization is a new task that localizes the best matched segment in an untrimmed video according to a given sentence query. In this localization task, one shou…
Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition
Miao Yin, Siyu Liao, Xiao-Yang Liu +2
Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model si…
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
Xinyi Li, Yinchuan Li, Hongyang Yang +2
Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent yea…
Spatial Influence-aware Reinforcement Learning for Intelligent Transportation System
Wenhang Bao, Xiao-yang Liu
Intelligent transportation systems (ITSs) are envisioned to be crucial for smart cities, which aims at improving traffic flow to improve the life quality of urban residents and red…
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
Wenhao Zhang, Wentian Bao, Xiao-Yang Liu +4
Post-click conversion rate (CVR) estimation is a critical task in e-commerce recommender systems. This task is deemed quite challenging under the industrial setting with two major…
Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction
Xinyi Li, Yinchuan Li, Xiao-Yang Liu +1
Midterm stock price prediction is crucial for value investments in the stock market. However, most deep learning models are essentially short-term and applying them to midterm pred…
Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis
Wenhang Bao, Xiao-yang Liu
Liquidation is the process of selling a large number of shares of one stock sequentially within a given time frame, taking into consideration the costs arising from market impact a…
Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation
Xinyi Li, Yinchuan Li, Yuancheng Zhan +1
Portfolio allocation is crucial for investment companies. However, getting the best strategy in a complex and dynamic stock market is challenging. In this paper, we propose a novel…
Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks
Ming Zhu, Xiao-Yang Liu, Anwar Walid
Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effect…
TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
Zihan Ding, Xiao-Yang Liu, Miao Yin +1
Deep generative models have been successfully applied to many applications. However, existing works experience limitations when generating large images (the literature usually gene…