1.2k citations · 2.1k across the 35 of their papers we have counts for
35 papers
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Zhining Liu, Pengfei Wei, Zhepei Wei +5
Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) methods balance the data via intuitive class-wise resampling or reweighting. Howe…
IMBENS: Ensemble Class-imbalanced Learning in Python
Zhining Liu, Jian Kang, Hanghang Tong +1
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides stand…
HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information
Wentao Xu, Weiqing Liu, Lewen Wang +4
Stock trend forecasting, which forecasts stock prices' future trends, plays an essential role in investment. The stocks in a market can share information so that their stock prices…
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
Wentao Xu, Weiqing Liu, Jiang Bian +2
The multivariate time series forecasting has attracted more and more attention because of its vital role in different fields in the real world, such as finance, traffic, and weathe…
Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation
Hengxu Lin, Dong Zhou, Weiqing Liu +1
Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance. Within the modern portfolio construction framework…
Deep Subdomain Adaptation Network for Image Classification
Yongchun Zhu, Fuzhen Zhuang, Jindong Wang +5
For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a…
Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study
Disheng Tang, Wei Cao, Jiang Bian +4
In late-2020, many countries around the world faced another surge in number of confirmed cases of COVID-19, including United Kingdom, Canada, Brazil, United States, etc., which res…
Universal Trading for Order Execution with Oracle Policy Distillation
Yuchen Fang, Kan Ren, Weiqing Liu +5
As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards eff…
Model Complexity of Deep Learning: A Survey
Xia Hu, Lingyang Chu, Jian Pei +2
Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complex…
REST: Relational Event-driven Stock Trend Forecasting
Wentao Xu, Weiqing Liu, Chang Xu +3
Stock trend forecasting, aiming at predicting the stock future trends, is crucial for investors to seek maximized profits from the stock market. Many event-driven methods utilized…
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations
Wenlei Shi, Xinran Wei, Jia Zhang +4
Multi-agent reinforcement learning (MARL) has been increasingly explored to learn the cooperative policy towards maximizing a certain global reward. Many existing studies take adva…
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting
Hongshun Tang, Lijun Wu, Weiqing Liu +1
Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field. Though deep learning methods have achieved promising resu…
COSEA: Convolutional Code Search with Layer-wise Attention
Hao Wang, Jia Zhang, Yingce Xia +3
Semantic code search, which aims to retrieve code snippets relevant to a given natural language query, has attracted many research efforts with the purpose of accelerating software…
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu, Pengfei Wei, Jing Jiang +3
Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed…
Qlib: An AI-oriented Quantitative Investment Platform
Xiao Yang, Weiqing Liu, Dong Zhou +2
Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments. Recently, inspired by rapid developmen…
Learning to Reweight with Deep Interactions
Yang Fan, Yingce Xia, Lijun Wu +5
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 t…
Measuring Model Complexity of Neural Networks with Curve Activation Functions
Xia Hu, Weiqing Liu, Jiang Bian +1
It is fundamental to measure model complexity of deep neural networks. The existing literature on model complexity mainly focuses on neural networks with piecewise linear activatio…
MC-BERT: Efficient Language Pre-Training via a Meta Controller
Zhenhui Xu, Linyuan Gong, Guolin Ke +5
Pre-trained contextual representations (e.g., BERT) have become the foundation to achieve state-of-the-art results on many NLP tasks. However, large-scale pre-training is computati…
Invertible Image Rescaling
Mingqing Xiao, Shuxin Zheng, Chang Liu +6
High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover t…
Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction
Chi Chen, Li Zhao, Wei Cao +2
Nowadays, machine learning methods have been widely used in stock prediction. Traditional approaches assume an identical data distribution, under which a learned model on the train…
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Derek Yang, Li Zhao, Zichuan Lin +3
Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-t…
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
Zhining Liu, Wei Cao, Zhifeng Gao +4
Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scal…
LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm
Ziyu Liu, Guolin Ke, Jiang Bian +1
Multiclass decomposition splits a multiclass classification problem into a series of independent binary learners and recomposes them by combining their outputs to reconstruct the m…
Light Multi-segment Activation for Model Compression
Zhenhui Xu, Guolin Ke, Jia Zhang +2
Model compression has become necessary when applying neural networks (NN) into many real application tasks that can accept slightly-reduced model accuracy with strict tolerance to…