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20192023
most citedCollaboration based Multi-Label Learning

5 citations · 13 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.LG2023

Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context

Haochong Xia, Shuo Sun, Xinrun Wang +1

Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financi…

cs.LG20231 cited

IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making

Hui Niu, Siyuan Li, Jiahao Zheng +4

Market making (MM) has attracted significant attention in financial trading owing to its essential function in ensuring market liquidity. With strong capabilities in sequential dec…

cs.LG2023

State Regularized Policy Optimization on Data with Dynamics Shift

Zhenghai Xue, Qingpeng Cai, Shuchang Liu +4

In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of cu…

cs.LG20195 cited

Collaboration based Multi-Label Learning

Lei Feng, Bo An, Shuo He

It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, whic…

cs.LG2019

Partial Label Learning with Self-Guided Retraining

Lei Feng, Bo An

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attemp…