Search Results for author: Weiqing Liu

Found 24 papers, 11 papers with code

Microstructure-Empowered Stock Factor Extraction and Utilization

no code implementations16 Aug 2023 Xianfeng Jiao, Zizhong Li, Chang Xu, Yang Liu, Weiqing Liu, Jiang Bian

To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios.

Stock Trend Prediction

Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance

no code implementations6 Jul 2023 Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu

Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.

Reinforcement Learning (RL)

Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems

no code implementations17 May 2023 Sarthak Ahuja, Mohammad Kachuee, Fateme Sheikholeslami, Weiqing Liu, Jaeyoung Do

Off-Policy reinforcement learning has been a driving force for the state-of-the-art conversational AIs leading to more natural humanagent interactions and improving the user satisfaction for goal-oriented agents.

Off-policy evaluation regression +1

Learning Differential Operators for Interpretable Time Series Modeling

no code implementations3 Sep 2022 Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian

In this work, we propose an learning framework that can automatically obtain interpretable PDE models from sequential data.

Decision Making Meta-Learning +2

Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble

no code implementations19 May 2022 Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li

Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.

reinforcement-learning Reinforcement Learning (RL)

DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

1 code implementation11 Jan 2022 Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian

To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.

Stock Prediction

SHGNN: Structure-Aware Heterogeneous Graph Neural Network

1 code implementation12 Dec 2021 Wentao Xu, Yingce Xia, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.

Graph Embedding Node Classification

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings

2 code implementations COLING 2022 Zhiping Luo, Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering.

Contrastive Learning Knowledge Graph Embedding +6

HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

2 code implementations26 Oct 2021 Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, Tie-Yan Liu

To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.

DM-CT: Consistency Training with Data and Model Perturbation

no code implementations29 Sep 2021 Xiaobo Liang, Runze Mao, Lijun Wu, Juntao Li, Weiqing Liu, Qing Li, Min Zhang

The common approach of consistency training is performed on the data-level, which typically utilizes the data augmentation strategy (or adversarial training) to make the predictions from the augmented input and the original input to be consistent, so that the model is more robust and attains better generalization ability.

Data Augmentation Image Classification +2

Deep Ensemble Policy Learning

no code implementations29 Sep 2021 Zhengyu Yang, Kan Ren, Xufang Luo, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li

Ensemble learning, which can consistently improve the prediction performance in supervised learning, has drawn increasing attentions in reinforcement learning (RL).

Ensemble Learning Reinforcement Learning (RL)

Instance-wise Graph-based Framework for Multivariate Time Series Forecasting

1 code implementation14 Sep 2021 Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting.

Multivariate Time Series Forecasting Time Series

Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation

no code implementations12 Jul 2021 Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian

Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance.

Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

2 code implementations24 Jun 2021 Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian

In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.

Stock Prediction

REST: Relational Event-driven Stock Trend Forecasting

no code implementations15 Feb 2021 Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu

To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.

Universal Trading for Order Execution with Oracle Policy Distillation

no code implementations28 Jan 2021 Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.

Algorithmic Trading reinforcement-learning +1

Learning to Use Future Information in Simultaneous Translation

1 code implementation1 Jan 2021 Xueqing Wu, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Tao Qin, Tie-Yan Liu

For wait-k inference, we observe that wait-m training with $m>k$ in simultaneous NMT (i. e., using more future information for training than inference) generally outperforms wait-k training.

Machine Translation NMT +1

ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting

1 code implementation11 Dec 2020 Hongshun Tang, Lijun Wu, Weiqing Liu, Jiang Bian

Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field.


Qlib: An AI-oriented Quantitative Investment Platform

2 code implementations22 Sep 2020 Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, Tie-Yan Liu

Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.

Portfolio Optimization Stock Market Prediction

Temporally Correlated Task Scheduling for Sequence Learning

2 code implementations10 Jul 2020 Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, Tie-Yan Liu

In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks, which are different in terms of how much input information to use or which future step to predict.

Machine Translation Scheduling +1

Learning to Reweight with Deep Interactions

no code implementations9 Jul 2020 Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, Xiang-Yang Li

Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.

Image Classification Machine Translation +1

Measuring Model Complexity of Neural Networks with Curve Activation Functions

no code implementations16 Jun 2020 Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei

Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.

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