暂无分享,去创建一个
Le Song | Alexander J. Smola | Andrew Gordon Wilson | Zichao Yang | Alex Smola | Le Song | Zichao Yang | A. Wilson
[1] Le Song,et al. Scalable Kernel Methods via Doubly Stochastic Gradients , 2014, NIPS.
[2] Radford M. Neal. Assessing Relevance determination methods using DELVE , 1998 .
[3] Andrew Gordon Wilson,et al. Gaussian Process Kernels for Pattern Discovery and Extrapolation , 2013, ICML.
[4] Amos Storkey,et al. Advances in Neural Information Processing Systems 20 , 2007 .
[5] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[6] I. J. Schoenberg. Positive definite functions on spheres , 1942 .
[7] AI Koan,et al. Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning , 2008, NIPS.
[8] Brian Kingsbury,et al. How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets , 2014, ArXiv.
[9] Alexander J. Smola,et al. Hyperkernels , 2002, NIPS.
[10] J. Mercer. Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .
[11] J. Mercer. Functions of positive and negative type, and their connection with the theory of integral equations , 1909 .
[12] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[13] Carl E. Rasmussen,et al. Sparse Spectrum Gaussian Process Regression , 2010, J. Mach. Learn. Res..
[14] Timothy J. Robinson,et al. Sequential Monte Carlo Methods in Practice , 2003 .
[15] Andrew Gordon Wilson,et al. Fast Kernel Learning for Multidimensional Pattern Extrapolation , 2014, NIPS.
[16] G. Wahba,et al. A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines , 1970 .
[17] 渡辺 亮平,et al. Sequential Monte Carlo , 2005, Nonlinear Time Series Analysis.
[18] Nathan Halko,et al. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..
[19] Alexander J. Smola,et al. Fastfood - Computing Hilbert Space Expansions in loglinear time , 2013, ICML.
[20] Thomas G. Dietterich,et al. Editors. Advances in Neural Information Processing Systems , 2002 .
[21] Bernhard Schölkopf,et al. A Generalized Representer Theorem , 2001, COLT/EuroCOLT.
[22] J. Halton. Sequential Monte Carlo , 1962, Mathematical Proceedings of the Cambridge Philosophical Society.
[23] Susan A. Murphy,et al. Monographs on statistics and applied probability , 1990 .
[24] Christopher K. I. Williams. Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.