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Kernel methods for big data:

31 March - 2 April 2014


Kernel methods for big data

new challenging problems


Location

The conference take places in amphi Appert of Polytech'Lille

Program (provisory)

31 March
  • 14h->15h: registration
  • 15h->16h30: Lecture J-Ph. Vert,
    Learning with kernels: an introduction (1/2)
  • 16h30->17h20: D. Sejdinovic,
    MCMC Kameleon: Kernel Adaptive Metropolis-Hastings
  • 17h20->18h10: C. Preda,
    RKHS methods for regression with functional data
1 April
  • 9h00->10h30: Lecture A. Gretton,
    A short introduction to reproducing kernel Hilbert spaces (1/3)
  • coffee break
  • 10h50->11h40: S. Arlot,
    Kernel change-point detection
  • 11h40-> 12h30: G. Rigail,
    Kernel-based multiple change-point detection
  • lunch
  • 14h30->16h: Lecture J-Ph. Vert,
    Learning with kernels: an introduction (2/2)
  • coffee break
  • 16h20->17h10: C. Bouveyron,
    Kernel discriminant analysis and clustering with parsimonious Gaussian process models
  • 17h10->18h00: J. Kellner,
    Normality test in high (or infinite) dimension with kernels
2 April
  • 9h00->10h30: Lecture A. Gretton,
    A short introduction to reproducing kernel Hilbert spaces (2/3)
  • coffee break
  • 10h50->11h40: Z. Szabo,
    Learning on Distributions
  • 11h40-> 12h30: R. Lajugie,
    Large margin metric learning for constraint partitioning problems
  • lunch
  • 14h30->16h: Lecture A. Gretton,
    A short introduction to reproducing kernel Hilbert spaces (3/3)
  • 16h00->16h50: F. Bach,
    Sharp analysis of low-rank kernel matrix approximation