Reinforcement Learning in NIPS 2018
阿新 • • 發佈：2018-12-29
- Aniket Bajpai, Sankalp Garg, and Mausam. Transfer of deep reactive policies for MDP planning.
- Liang-Chieh Chen, Maxwell Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, and Jon Shlens. Searching for efficient multi-scale architectures for dense image prediction.
- Tao Chen, Adithyavairavan Murali, and Abhinav Gupta. Hardware conditioned policies for multi-robot transfer learning.
- Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. Learning to optimize tensor programs.
- Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel. Evolved policy gradients.
- Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric Xing. Neural architecture search with Bayesian optimisation and optimal transport.
- Shichen Liu, Mingsheng Long, Jianmin Wang, and Michael Jordan. Generalized zero-shot learning with deep calibration network.
- Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tieyan Liu. Neural architecture optimization.
- Ofir Marom and Benjamin Rosman. Zero-shot transfer with deictic object-oriented representation in reinforcement learning.
- Massimiliano Pontil, Giulia Denevi, Carlo Ciliberto, and Dimitris Stamos. Learning to learn around a common mean.
- Ozan Sener, Ozan Sener, and Vladlen Koltun. Multi-task learning as multi-objective optimization.
- Sungryull Sohn, Junhyuk Oh, and Honglak Lee. Multitask reinforcement learning for zero-shot generalization with subtask dependencies.
- Bradly Stadie, Ge Yang, Pieter Abbeel, Yuhuai Wu, Yan Duan, Xi Chen, Rein Houthooft, and Ilya Sutskever. The importance of sampling in meta- reinforcement learning.
- Andrea Tirinzoni, Rafael Rodriguez, and Marcello Restelli. Transfer of value functions via variational methods.
- Rasul Tutunov, Dongho Kim, and Haitham Bou Ammar. Distributed multitask reinforcement learning with quadratic convergence.
- Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, and Swarat Chaudhuri. Synthesis of differentiable functional programs for lifelong learning.
- Tongzhou Wang, YI WU, David Moore, and Stuart Russell. Meta-learning MCMC proposals.
- Catherine Wong, Neil Houlsby, Yifeng Lu, and Andrea Gesmundo. Transfer learning with neural AutoML.
- Ju Xu and Zhanxing Zhu. Reinforced continual learning.
- Kelvin Xu, Chelsea Finn, and Sergey Levine. Uncertainty-aware few-shot learning with probabilistic model-agnostic meta-learning.
- Zhongwen Xu, Hado van Hasselt, and David Silver. Meta-gradient reinforcement learning.
- Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn. Bayesian model-agnostic meta-learning.
- Yu Zhang, Ying Wei, and Qiang Yang. Learning to multitask.
- Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, Joao P Costeira, and Geoffrey Gordon. Adversarial multiple source domain adaptation.