Search Results for author: Xiao-Yang Liu

Found 46 papers, 19 papers with code

Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English

1 code implementation12 Feb 2024 Xiao Zhang, Ruoyu Xiang, Chenhan Yuan, Duanyu Feng, Weiguang Han, Alejandro Lopez-Lira, Xiao-Yang Liu, Sophia Ananiadou, Min Peng, Jimin Huang, Qianqian Xie

We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.

Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning

no code implementations29 Dec 2023 Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao, Shan Zhong, Matt White, Meikang Qiu

The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science.

Federated Learning Language Modelling +1

FinGPT: Democratizing Internet-scale Data for Financial Large Language Models

1 code implementation19 Jul 2023 Xiao-Yang Liu, Guoxuan Wang, Hongyang Yang, Daochen Zha

In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity.

Algorithmic Trading Sentiment Analysis

Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models

1 code implementation22 Jun 2023 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.

Sentiment Analysis

FinGPT: Open-Source Financial Large Language Models

2 code implementations9 Jun 2023 Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang

While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize Internet-scale financial data.

Algorithmic Trading Language Modelling +1

Dynamic Datasets and Market Environments for Financial Reinforcement Learning

4 code implementations25 Apr 2023 Xiao-Yang Liu, Ziyi Xia, Hongyang Yang, Jiechao Gao, Daochen Zha, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo

The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets.

reinforcement-learning

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems

no code implementations4 Feb 2023 Xiao-Yang Liu, Ming Zhu, Sem Borst, Anwar Walid

In this paper, we investigate deep reinforcement learning to control traffic lights, and both theoretical analysis and numerical experiments show that the intelligent behavior ``greenwave" (i. e., a vehicle will see a progressive cascade of green lights, and not have to brake at any intersection) emerges naturally a grid road network, which is proved to be the optimal policy in an avenue with multiple cross streets.

reinforcement-learning Reinforcement Learning (RL)

FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning

4 code implementations6 Nov 2022 Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo

However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage.

reinforcement-learning Reinforcement Learning (RL)

ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning

1 code implementation11 Dec 2021 Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael I. Jordan

In this paper, we present a scalable and elastic library ElegantRL-podracer for cloud-native deep reinforcement learning, which efficiently supports millions of GPU cores to carry out massively parallel training at multiple levels.

reinforcement-learning Reinforcement Learning (RL) +1

FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

no code implementations7 Nov 2021 Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, Christina Dan Wang

In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve.

Friction reinforcement-learning +1

Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach

no code implementations7 Nov 2021 Mao Guan, Xiao-Yang Liu

In particular, we quantify the prediction power by calculating the linear correlations between the feature weights of a DRL agent and the reference feature weights, and similarly for machine learning methods.

Management reinforcement-learning +1

FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance

no code implementations7 Nov 2021 Zechu Li, Xiao-Yang Liu, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo

Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading.

reinforcement-learning Reinforcement Learning (RL) +1

Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure

no code implementations CVPR 2021 Miao Yin, Siyu Liao, Xiao-Yang Liu, Xiaodong Wang, Bo Yuan

Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in resource-restricted environments is still a very challenging problem.

Tensor Decomposition Video Recognition

Convolutional Graph-Tensor Net for Graph Data Completion

no code implementations7 Mar 2021 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.

Recommendation Systems

Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification

1 code implementation8 Jan 2021 Fanjie Kong, Xiao-Yang Liu, Ricardo Henao

In the end, our experimental results indicate that tensor network models are effective for tiny object classification problem and potentially will beat state-of-the-art.

BIG-bench Machine Learning Classification +4

Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

no code implementations10 Dec 2020 Daizong Liu, Shuangjie Xu, Xiao-Yang Liu, Zichuan Xu, Wei Wei, Pan Zhou

To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph.

Graph Neural Network Object +3

FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

6 code implementations19 Nov 2020 Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang, Bowen Xiao, Christina Dan Wang

In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.

reinforcement-learning Reinforcement Learning (RL) +1

Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization

1 code implementation4 Aug 2020 Daizong Liu, Xiaoye Qu, Xiao-Yang Liu, Jianfeng Dong, Pan Zhou, Zichuan Xu

To this end, we propose a novel Cross- and Self-Modal Graph Attention Network (CSMGAN) that recasts this task as a process of iterative messages passing over a joint graph.

Graph Attention Sentence

DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News

4 code implementations20 Dec 2019 Xinyi Li, Yinchuan Li, Hongyang Yang, Liuqing Yang, Xiao-Yang Liu

In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism.

Stock Prediction Stock Price Prediction

Spatial Influence-aware Reinforcement Learning for Intelligent Transportation System

no code implementations14 Dec 2019 Wenhang Bao, Xiao-Yang Liu

We demonstrate three types of directed communications to show the effect of directions of social influence on the entire network utility and individual utility.

reinforcement-learning Reinforcement Learning (RL)

Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction

no code implementations3 Aug 2019 Xinyi Li, Yinchuan Li, Xiao-Yang Liu, Christina Dan Wang

In this paper, we propose a novel deep neural network Mid-LSTM for midterm stock prediction, which incorporates the market trend as hidden states.

Management Stock Prediction +1

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis

5 code implementations24 Jun 2019 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 and a trader's risk aversion.

reinforcement-learning Reinforcement Learning (RL)

Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks

no code implementations12 Jun 2019 Ming Zhu, Xiao-Yang Liu, Xiaodong Wang

Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities.

reinforcement-learning Reinforcement Learning (RL)

TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation

1 code implementation28 Jan 2019 Zihan Ding, Xiao-Yang Liu, Miao Yin, Linghe Kong

Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions.

Dictionary Learning Image Generation +1

Deep Reinforcement Learning for Intelligent Transportation Systems

no code implementations3 Dec 2018 Xiao-Yang Liu, Zihan Ding, Sem Borst, Anwar Walid

Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe.

Management reinforcement-learning +1

Practical Deep Reinforcement Learning Approach for Stock Trading

9 code implementations19 Nov 2018 Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong, Hongyang Yang, Anwar Walid

We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return.

reinforcement-learning Reinforcement Learning (RL)

Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis

no code implementations18 Nov 2018 Weijun Lu, Xiao-Yang Liu, Qingwei Wu, Yue Sun, Anwar Walid

We propose a novel multilinear dynamical system (MLDS) in a transform domain, named $\mathcal{L}$-MLDS, to model tensor time series.

Time Series Time Series Analysis

Information Scaling Law of Deep Neural Networks

no code implementations13 Feb 2018 Xiao-Yang Liu

In this paper, we propose a novel information scaling law scheme that can interpret the network's inner organization by information theory.

An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems

no code implementations27 Dec 2017 Ming Zhu, Xiao-Yang Liu, Xiaodong Wang

As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities.

Management Scheduling

Multidimensional Data Tensor Sensing for RF Tomographic Imaging

no code implementations13 Dec 2017 Tao Deng, Xiao-Yang Liu, Feng Qian, Anwar Walid

The recently proposed transform-based tensor model is more appropriate for sensory data processing, as it helps exploit the geometric structures of the three-dimensional target and improve the recovery precision.

Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization

no code implementations7 Nov 2017 Chenxiao Zhu, Lingqing Xu, Xiao-Yang Liu, Feng Qian

Global Positioning System (GPS) becomes invalid in indoor environments due to the non-line-of-sight issue, so it is urgent to develop a real-time high-accuracy localization approach for smartphones.

Generative Adversarial Network Indoor Localization +1

Fourth-order Tensors with Multidimensional Discrete Transforms

4 code implementations3 May 2017 Xiao-Yang Liu, Xiaodong Wang

The multidimensional feature and huge volume of big data put urgent requirements to the development of multilinear modeling tools and efficient algorithms.

Numerical Analysis Information Theory Information Theory

3D seismic data denoising using two-dimensional sparse coding scheme

no code implementations8 Apr 2017 Ming-Jun Su, Jingbo Chang, Feng Qian, Guangmin Hu, Xiao-Yang Liu

Seismic data denoising is vital to geophysical applications and the transform-based function method is one of the most widely used techniques.

Denoising Vocal Bursts Valence Prediction

Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination

no code implementations28 Mar 2017 Fei Jiang, Xiao-Yang Liu, Hongtao Lu, Ruimin Shen

Sparse coding (SC) is an automatic feature extraction and selection technique that is widely used in unsupervised learning.

Denoising Vocal Bursts Valence Prediction

Graph Regularized Tensor Sparse Coding for Image Representation

no code implementations27 Mar 2017 Fei Jiang, Xiao-Yang Liu, Hongtao Lu, Ruimin Shen

Sparse coding (SC) is an unsupervised learning scheme that has received an increasing amount of interests in recent years.

Clustering Image Clustering

Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization

no code implementations10 Aug 2015 Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang, Min-You Wu

In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy.

Indoor Localization

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