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20162025
most citedLow-tubal-rank Tensor Completion using Alternating Minimization

56 citations · 250 across the 25 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

q-fin.ST2019★ 19 cited

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…

cs.MA2019

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…

cs.IR2019

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…

q-fin.ST2019

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…

q-fin.TR2019★ 11 cited

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…

q-fin.ST2019★ 9 cited

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…