Provably efficient learning with typed parametric models

被引：0
|
作者：
Brunskill, Emma [1 ]
Leffler, Bethany R. [1 ]
Li, Hong [1 ]
Littman, Michael L. [2 ]
Roy, Nicholas [2 ]
机构：
[1] Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02143, United States
[2] Department of Computer Science, Rutgers University Piscataway, NJ 08854, United States
来源：
Journal of Machine Learning Research | 2009年 / 10卷
关键词：
Compendex;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Markov processes
引用
收藏
页码：1955 / 1988
相关论文
共 50 条
  • [1] Provably Efficient Learning with Typed Parametric Models
    Brunskill, Emma
    Leffler, Bethany R.
    Li, Lihong
    Littman, Michael L.
    Roy, Nicholas
    JOURNAL OF MACHINE LEARNING RESEARCH, 2009, 10 : 1955 - 1988
  • [2] Provably Efficient Learning of Transferable Rewards
    Metelli, Alberto Maria
    Ramponi, Giorgia
    Concetti, Alessandro
    Restelli, Marcello
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, 2021, 139
  • [3] Is Q-learning Provably Efficient?
    Jin, Chi
    Allen-Zhu, Zeyuan
    Bubeck, Sebastien
    Jordan, Michael I.
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [4] Learning Topic Models - Provably and Efficiently
    Arora, Sanjeev
    Ge, Rong
    Halpern, Yoni
    Mimno, David
    Moitra, Ankur
    Sontag, David
    Wu, Yichen
    Zhu, Michael
    COMMUNICATIONS OF THE ACM, 2018, 61 (04) : 85 - 93
  • [5] Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
    Karlsson, Rickard K. A.
    Willbo, Martin
    Hussain, Zeshan
    Krishnan, Rahul G.
    Sontag, David
    Johansson, Fredrik D.
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151, 2022, 151
  • [6] Is Pessimism Provably Efficient for Offline Reinforcement Learning?
    Jin, Ying
    Yang, Zhuoran
    Wang, Zhaoran
    MATHEMATICS OF OPERATIONS RESEARCH, 2024,
  • [7] Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
    Feng, Fei
    Wang, Ruosong
    Yin, Wotao
    Du, Simon S.
    Yang, Lin F.
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [8] Towards provably efficient quantum algorithms for large-scale machine-learning models
    Liu, Junyu
    Liu, Minzhao
    Liu, Jin-Peng
    Ye, Ziyu
    Wang, Yunfei
    Alexeev, Yuri
    Eisert, Jens
    Jiang, Liang
    NATURE COMMUNICATIONS, 2024, 15 (01)
  • [9] Towards provably efficient quantum algorithms for large-scale machine-learning models
    Junyu Liu
    Minzhao Liu
    Jin-Peng Liu
    Ziyu Ye
    Yunfei Wang
    Yuri Alexeev
    Jens Eisert
    Liang Jiang
    Nature Communications, 15
  • [10] A Provably Efficient Sample Collection Strategy for Reinforcement Learning
    Tarbouriech, Jean
    Pirotta, Matteo
    Valko, Michal
    Lazaric, Alessandro
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
12345