暂无分享,去创建一个
[1] M. Rosenblatt. Remarks on Some Nonparametric Estimates of a Density Function , 1956 .
[2] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[3] E. Nadaraya. On Estimating Regression , 1964 .
[4] G. S. Watson,et al. Smooth regression analysis , 1964 .
[5] W. Härdle,et al. Asymptotic nonequivalence of some bandwidth selectors in nonparametric regression , 1985 .
[6] H. Müller,et al. Convolution type estimators for nonparametric regression , 1988 .
[7] T. Gasser,et al. Choice of bandwidth for kernel regression when residuals are correlated , 1992 .
[8] Jianqing Fan,et al. Variable Bandwidth and Local Linear Regression Smoothers , 1992 .
[9] Jianqing Fan. Design-adaptive Nonparametric Regression , 1992 .
[10] Ravi Bansal,et al. Nonparametric estimation of structural models for high-frequency currency market data , 1995 .
[11] B. P. Zhang,et al. Estimation of the Lipschitz constant of a function , 1996, J. Glob. Optim..
[12] B. Ray,et al. Bandwidth selection for kernel regression with long-range dependent errors , 1997 .
[13] Adam Krzyzak,et al. A Distribution-Free Theory of Nonparametric Regression , 2002, Springer series in statistics.
[14] Pierre Geurts,et al. Tree-Based Batch Mode Reinforcement Learning , 2005, J. Mach. Learn. Res..
[15] David J. Fleet,et al. Gaussian Process Dynamical Models , 2005, NIPS.
[16] L. Wasserman. All of Nonparametric Statistics , 2005 .
[17] Jan Peters,et al. Using model knowledge for learning inverse dynamics , 2010, 2010 IEEE International Conference on Robotics and Automation.
[18] Oliver Kroemer,et al. A Non-Parametric Approach to Dynamic Programming , 2011, NIPS.
[19] Carl E. Rasmussen,et al. PILCO: A Model-Based and Data-Efficient Approach to Policy Search , 2011, ICML.
[20] Oliver Kroemer,et al. A kernel-based approach to direct action perception , 2012, 2012 IEEE International Conference on Robotics and Automation.
[21] Anja Schindler,et al. A Review and Comparison of Bandwidth Selection Methods for Kernel Regression , 2014 .
[22] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[23] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Wojciech Zaremba,et al. OpenAI Gym , 2016, ArXiv.
[25] Moses Charikar,et al. Hashing-Based-Estimators for Kernel Density in High Dimensions , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).
[26] Dirk Ormoneit,et al. Kernel-Based Reinforcement Learning , 2017, Encyclopedia of Machine Learning and Data Mining.
[27] Piotr Indyk,et al. Space and Time Efficient Kernel Density Estimation in High Dimensions , 2019, NeurIPS.
[28] Jan Peters,et al. A Nonparametric Off-Policy Policy Gradient , 2020, AISTATS.