## 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*