Hongliang Li

Affiliations:
  • Tencent Inc., AI Platform Department, Shenzhen, China
  • IBM Research-China, Beijing, China (2015 - 2016)
  • Chinese Academy of Sciences, Institute of Automation, Beijing, China (PhD 2015)


According to our database1, Hongliang Li authored at least 10 papers between 2012 and 2016.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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PhD thesis 
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Bibliography

2016
A big data based product ranking solution.
Proceedings of the 2016 IEEE International Conference on Service Operations and Logistics, 2016

Manifold regularization based approximate value iteration for learning control.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

2015
Approximate policy iteration with unsupervised feature learning based on manifold regularization.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Neural-network-based finite horizon optimal control for partially unknown linear continuous-time systems.
Proceedings of the Seventh International Conference on Advanced Computational Intelligence, 2015

Adaptive dynamic programming for infinite horizon optimal robust guaranteed cost control of a class of uncertain nonlinear systems.
Proceedings of the American Control Conference, 2015

2014
Data-driven iterative adaptive dynamic programming algorithm for approximate optimal control of unknown nonlinear systems.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Output-based high-order bipartite consensus under directed antagonistic networks.
Proceedings of the 2014 IEEE Symposium on Intelligent Agents, 2014

2013
Integral Policy Iteration for Zero-Sum Games with Completely Unknown Nonlinear Dynamics.
Proceedings of the Neural Information Processing - 20th International Conference, 2013

Adaptive Dynamic Programming for Solving Non-Zero-Sum Differential Games.
Proceedings of the 3rd IFAC International Conference on Intelligent Control and Automation Science, 2013

2012
H∞ control of unknown discrete-time nonlinear systems with control constraints using adaptive dynamic programming.
Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012


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