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Scaling Multidimensional Inference for Structured Gaussian Processes
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Elad Gilboa,Yunus Saatci,John P. Cunningham
Issue Date:October 2013
pp. 1
Exact Gaussian process (GP) regression has O(N^3) runtime for data size N, making it intractable for large N. Many algorithms for improving GP scaling approximate the covariance with lower rank matrices. Other work has exploited structure inherent in parti...
 
Workshop summary: Numerical mathematics in machine learning
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By John P. Cunningham, Matthias Seeger, Suvrit Sra
Issue Date:June 2009
pp. 1-1
Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time complexity of standard interior-point SDP solvers could be as high as O(n6.5). Such inte...
     
Fast Gaussian process methods for point process intensity estimation
Found in: Proceedings of the 25th international conference on Machine learning (ICML '08)
By John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
Issue Date:July 2008
pp. 192-199
Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gaussian Processes offer an attractive framework within which to infer underlying intensity functions. The resu...
     
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