Miao Liu

Orcid: 0000-0002-1648-8325

Affiliations:
  • IBM T. J. Watson Research Center,Yorktown Heights, NY, USA
  • MIT, Laboratory for Information and Decision Systems, Cambridge, MA, USA (former)
  • Duke University, Durham, NC, USA (PhD 2014)


According to our database1, Miao Liu authored at least 37 papers between 2011 and 2023.

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

Timeline

Legend:

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Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2023
Learning in Factored Domains with Information-Constrained Visual Representations.
CoRR, 2023

2022
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria.
CoRR, 2022

AI Planning Annotation for Sample Efficient Reinforcement Learning.
CoRR, 2022

Influencing Long-Term Behavior in Multiagent Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Cost-Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment.
Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

Context-Specific Representation Abstraction for Deep Option Learning.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games.
Proceedings of the 2021 IEEE Conference on Games (CoG), 2021

RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract).
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games.
CoRR, 2020

Deep RL With Information Constrained Policies: Generalization in Continuous Control.
CoRR, 2020

Learning Hierarchical Teaching Policies for Cooperative Agents.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

On the Role of Weight Sharing During Deep Option Learning.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Automatic Pan-Tilt Camera Control for Learning Dirichlet Process Gaussian Process (DPGP) Mixture Models of Multiple Moving Targets.
IEEE Trans. Autom. Control., 2019

Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning.
CoRR, 2019

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.
Proceedings of the 7th International Conference on Learning Representations, 2019

Learning to Teach in Cooperative Multiagent Reinforcement Learning.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Learning Abstract Options.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Eigenoption Discovery through the Deep Successor Representation.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
The Eigenoption-Critic Framework.
CoRR, 2017

Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions.
Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017

Socially aware motion planning with deep reinforcement learning.
Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017

Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations.
Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Scalable accelerated decentralized multi-robot policy search in continuous observation spaces.
Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning.
Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Quickest change detection approach to optimal control in Markov decision processes with model changes.
Proceedings of the 2017 American Control Conference, 2017

2016
Information value in nonparametric Dirichlet-process Gaussian-process (DPGP) mixture models.
Autom., 2016

Reports of the AAAI 2016 Spring Symposium Series.
AI Mag., 2016

Motion planning with diffusion maps.
Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

Augmented dictionary learning for motion prediction.
Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Stick-Breaking Policy Learning in Dec-POMDPs.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

2014
Efficient Bayesian Nonparametric Methods for Model-Free Reinforcement Learning in Centralized and Decentralized Sequential Environments.
PhD thesis, 2014

2013
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
CoRR, 2013

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Online Expectation Maximization for Reinforcement Learning in POMDPs.
Proceedings of the IJCAI 2013, 2013

2011
The Infinite Regionalized Policy Representation.
Proceedings of the 28th International Conference on Machine Learning, 2011


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