Gatsby

Results: 528



#Item
101

Learning from Features of Sets and Probabilities Zolt´an Szab´ o (Gatsby Unit, UCL) Department of Computing Imperial College London

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Source URL: www.gatsby.ucl.ac.uk

Language: English - Date: 2016-03-10 14:51:43
    102

    Optimal Rates for the Random Fourier Feature Method Zolt´an Szab´o∗ Gatsby Unit, UCL Joint work with Bharath K. Sriperumbudur∗

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    Source URL: www.gatsby.ucl.ac.uk

    Language: English - Date: 2015-12-04 13:03:10
      103

      Optimal Rates for the Random Fourier Feature Technique∗ Zolt´an Szab´o (Gatsby Unit, University College London) Abstract

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      Source URL: www.gatsby.ucl.ac.uk

      Language: English - Date: 2016-03-10 15:54:16
        104

        Nonparametric Independence Tests: Space Partitioning and Kernel Approaches Arthur Gretton1 and L´ aszl´o Gy¨orfi2 1

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        Source URL: www.gatsby.ucl.ac.uk

        Language: English - Date: 2010-08-06 08:10:12
          105

          Feature space Basics of reproducing kernel Hilbert spaces Kernel Ridge Regression Lecture 1: Introduction to RKHS MLSS Tübingen, 2015

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          Source URL: www.gatsby.ucl.ac.uk

          Language: English - Date: 2015-07-22 19:18:50
            106

            Learning with probabilities as inputs, using kernels Arthur Gretton Gatsby Computational Neuroscience Unit NIPS workshop on Probabilistic Numerics, 2015

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            Source URL: www.gatsby.ucl.ac.uk

            Language: English - Date: 2016-02-23 08:34:50
              107

              ∗ Optimal Rates for Random Fourier Features Bharath K. Sriperumbudur Zoltán Szabó

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              Source URL: www.gatsby.ucl.ac.uk

              Language: English - Date: 2015-11-13 20:51:57
                108

                Distribution Regression: Computational & Statistical Tradeoffs Zolt´an Szab´o (Gatsby Unit, UCL) Joint work with ◦ Bharath K. Sriperumbudur (Department of Statistics, PSU),

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                Source URL: www.gatsby.ucl.ac.uk

                Language: English - Date: 2015-11-27 12:10:48
                  109

                  Learning from Features of Sets and Probabilities∗ Zolt´an Szab´o (Gatsby Unit, University College London) Abstract Many modern learning problems make inferences based on probabilities as inputs. For example, Microso

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                  Source URL: www.gatsby.ucl.ac.uk

                  Language: English - Date: 2016-03-02 19:46:11
                    110

                    Nonparametric Tree Graphical Models via Kernel Embeddings 1 Le Song,1 Arthur Gretton,1,2 Carlos Guestrin1 School of Computer Science, Carnegie Mellon University; 2 MPI for Biological Cybernetics

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                    Source URL: www.gatsby.ucl.ac.uk

                    Language: English - Date: 2010-08-06 08:10:05
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