Xiao-Yang (Yanglet) Liu

( * indicates corresponding author)

Financial Large Language Models (FinLLM)

• Xiao-Yang Liu, Jie Zhang, Guoxuan Wang, Weiqing Tong, Anwar Walid. Efficient Pretraining and Finetuning of Quantized LLMs with Low-rank Structure. (arXiv version: FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing). IEEE ICDCS 2024.
• Xiao-Yang Liu, Guoxuan Wang, Hongyang Yang, and Daochen Zha*. Data-centric FinGPT: Democratizing Internet-scale data for financial large language models. Workshop on Instruction Tuning and Instruction Following, NeurIPS 2023.
• Xiao-Yang Liu, R. Zhu, Daochen Zha, J. Gao, S. Zhong, Meikang Qiu. Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning. arXiv, Dec., 2023.
• Xiao-Yang Liu, Y. Zhang, Y. Liao, and Ling Jiang*. Dynamic updating of the knowledge base for a large-scale question answering system. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 2020.
• Matt White, Ibrahim Haddad, Cailean Osborne, Xiao-Yang (Yanglet) Liu, Ahmed Abdelmonsef, Sachin Varghess. The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency and Usability in AI, 2024.
• Qianqian Xie, W. Han, Z. Chen, R. Xiang, X. Zhang, Y. He, M. Xiao, D. Li, Y. Dai, D. Feng, Y. Xu, Haoqiang Kang, Z. Kuang, C. Yuan, K. Yang, Z. Luo, T. Zhang, Z. Liu, G. Xiong, Z. Deng, Y. Jiang, Z. Yao, H. Li, Y. Yu, G. Hu, J. Huang, Xiao-Yang Liu, Alejandro Lopez-Lira, B. Wang, Y. Lai, H. Wang, M. Peng, Sophia Ananiadou, Jimin Huang. The FinBen: An Holistic Financial Benchmark for Large Language Models. 2024.
• X. Zhang, R. Xiang, C. Yuan, D. Feng, W. Han, Alejandro Lopez-Lira, Xiao-Yang Liu, Sophia Ananiadou, Min Peng, Jimin Huang, Qianqian Xie*. Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English. 2024.
• Haoqiang Kang and Xiao-Yang Liu*. Deficiency of large language models in finance: An empirical examination of hallucination. Workshop on Failure Modes in the Age of Foundation Models, NeurIPS 2023.
• Hongyang Yang, Xiao-Yang Liu*, and Christina Dan Wang. FinGPT: Open-source financial large language models. Symposium on FinLLM, IJCAI 2023. (Best Presentation Award)
• Boyu Zhang, Hongyang Yang and Xiao-Yang Liu*, Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models. Symposium on FinLLM, IJCAI 2023.
• Boyu Zhang, Hongyang Yang, Tianyu Zhou, Muhammad Ali Babar, and Xiao-Yang Liu*. Enhancing financial sentiment analysis via retrieval augmented large language models. ACM International Conference on AI in Finance (ICAIF), pp. 349-356, 2023.
• C. Zhang, Z. Wang, Liuqing Yang, Xiao-Yang Liu, and Ling Jiang*. Domain-specific sentence encoder for intention recognition in large-scale shopping platforms. International Conference on Knowledge Science, Engineering and Management, Part of the Lecture Notes in Computer Science book series (LNAI, volume 12816), Springer, 2021.

Financial Reinforcement Learning (FinRL)

• Xiao-Yang Liu, Z. Xia, H. Yang, J. Gao, D. Zha, M. Zhu, C.D. Wang, Z. Wang, and J. Guo. Dynamic Datasets and Market Environments for Financial Reinforcement Learning. Machine Learning Journal, Springer Nature, 2023.
• Xiao-Yang Liu, Z. Xia, J. Rui, J. Gao, H. Yang, M. Zhu, C.D. Wang, Z. Wang, and J. Guo. FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning. Advances in Neural Information Processing Systems (NeurIPS), 2022.
• Xiao-Yang Liu, Hongyang Yang, J. Gao, and C. D. Wang. FinRL: Deep reinforcement learning framework to automate trading in quantitative finance. ACM International Conference on AI in Finance (ICAIF), 2021.
• Xiao-Yang Liu, Z. Xiong, S. Zhong, Hongyang Yang, and Anwar Walid. Practical deep reinforcement learning approach for stock trading. Workshop on Challenges and Opportunities for AI in Financial Services, NeurIPS 2018.
• Kent Wu, Ziyi Xia, Shuaiyu Chen, and Xiao-Yang Liu. Curriculum Learning from Smart Retail Investors: Towards Financial Open-endedness. Agent Learning in Open-Endedness Workshop, NeurIPS 2023.
• Berend Jelmer D. Gort, Xiao-Yang Liu*, J. Gao, Shuaiyu Chen, and Christina Dan Wang. Deep reinforcement learning for cryptocurrency trading: Practical approach to address backtest overfitting. ACM International Conference on AI in Finance (ICAIF), Workshop on Benchmarks for AI in Finance, 2022; Also appeared at AAAI’23 Bridge on AI for Financial Services, 2023.  FinRL-Crypto.
• Z. Li, Xiao-Yang Liu*, J. Zheng, Z. Wang, Anwar Walid, and Jian Guo. FinRL-Podracer: High performance and scalable deep reinforcement learning for quantitative finance. ACM International Conference on AI in Finance (ICAIF), 2021.
• M. Guan and Xiao-Yang Liu*. Explainable deep reinforcement learning for portfolio management: An empirical approach. ACM International Conference on AI in Finance (ICAIF),  2021.
• Hongyang Yang, Xiao-Yang Liu*, S. Zhong, and Anwar Walid. Deep reinforcement learning for automated stock trading: an ensemble strategy. ACM International Conference on AI in Finance (ICAIF),  2020.
• Q. Chen and Xiao-Yang Liu*, Quantifying ESG alpha using scholar big data: An automated machine learning approach. ACM International Conference on AI in Finance (ICAIF), 2020.

Machine Learning and Quantum Machine Learning

• [Book Chapter] Xiao-Yang Liu. Chapters 2 and 7 of Reinforcement learning for cyber-physical systems: with cybersecurity case studies. Chapman & Hall/CRC, 2019.
• Xiao-Yang Liu and Zeliang Zhang. Classical simulation of quantum circuits using reinforcement learning: parallel environments and benchmark. NeurIPS, 2023. [Slides and Video]
• Xiao-Yang Liu, Z. Li, and Xiaodong Wang. Homomorphic matrix completion. Advances in Neural Information Processing Systems (NeurIPS), 2022.
• Xiao-Yang Liu, Q. Huang, X. Han, B. Wu, L. Kong, Anwar Walid, and Xiaodong Wang*. Real-time decoding of snapshot compressive imaging using tensor FISTA-Net. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.
• Xiao-Yang Liu and Xiaodong Wang∗. Real-time indoor localization for smartphones using tensor-generative adversarial nets. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 32(8):3433–3443, 2020.
• Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal*, and Xiaodong Wang. Low-tubal-rank tensor completion using alternating minimization. IEEE Transactions on Information Theory (TIT), 2020.
• Xiao-Yang Liu and Xiaodong Wang*. LS-decomposition for robust recovery of sensory big data. IEEE Transactions on Big Data, 2017.
• Jeremy Johnston, Xiao-Yang Liu, S. Wu, and Xiaodong Wang. A curriculum learning approach to optimization with application to downlink beamforming. IEEE Transactions on Signal Processing (TSP), 2023.
• Guangchen Lan, Xiao-Yang Liu, Y. Zhang, and Xiaodong Wang. Communication-efficient federated learning for resource-constrained edge devices. IEEE Transactions on Machine Learning in Communications and Networking, 2023.
• R. She, P. Fan*, Xiao-Yang Liu, Xiaodong Wang. Interpretable generative adversarial networks with exponential function. IEEE Transactions on Signal Processing (TSP), 2021.
• Miao Yin, S. Liao, Xiao-Yang Liu, Xiaodong Wang, and Bo Yuan. Towards extremely compact rnns for video recognition with fully decomposed hierarchical tucker structure. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
• Jiawei Ma, Xiao-Yang Liu, Zhen Shou, and X. Yuan. Deep tensor ADMM-net for snapshot compressive imaging. IEEE/CVF International Conference on Computer Vision (ICCV), 2019.

High-performance Computing and Quantum Computing

• [Book Chapter] Xiao-Yang Liu, Y. Fang, L. Yang, Z. Li, Anwar Walid. High-performance tensor decompositions for compressing and accelerating deep neural networks. Tensors for Data Processing: Theory, Methods, and Applications. Elsevier, 2021.
• Xiao-Yang Liu, Z. Zhang, Z. Wang, Xiaodong Wang, and A. Walid. High-performance tensor learning primitives using GPU tensor cores. IEEE Transactions on Computers, 2022.
• H. Huang, Xiao-Yang Liu*, W. Tong, T. Zhang, A. Walid, and Xiaodong Wang. High-performance hierarchical Tucker tensor learning using GPU tensor cores. IEEE Transactions on Computers, 2022.
• T. Zhang, W. Kan, and Xiao-Yang Liu*. High-performance GPU primitives for graph-tensor learning operations. Journal of Parallel and Distributed Computing, 148:125–137, 2021.
• T. Zhang, Xiao-Yang Liu*, Xiaodong Wang, and A. Walid. cuTensor-Tubal: Efficient primitives for tubal-rank tensor learning operations on GPUs. IEEE Transactions on Parallel and Distributed Systems, 31(3):595–610, 2019.
• Xiao-Yang Liu, H. Hong, Z. Zhang, W. Tong, X. Kossaifi, Xiaodong Wang*, and A. Walid. High-performance tensor-train and tensor-ring learning primitives using GPU tensor cores. (Major Revision) IEEE Transactions on Computers, 2022.
• Xiao-Yang Liu, Z. Li, Z. Yang, J. Zheng, Z. Wang, A. Walid, J. Guo, and Michael Jordan. ElegantRL-Podracer: Scalable and elastic library for cloud-native deep reinforcement learning. Deep RL Workshop, NeurIPS, 2021.
• W. Xu and Xiao-Yang Liu*. Classical simulation of sycamore quantum supremacy circuits using gpu tensor cores: A tensor network approach. ACM Student Research Competition at ICCAD, 2022.