C
Christian Walder
Researcher at Australian National University
Publications - 58
Citations - 437
Christian Walder is an academic researcher from Australian National University. The author has contributed to research in topics: Gaussian process & Computer science. The author has an hindex of 11, co-authored 51 publications receiving 390 citations. Previous affiliations of Christian Walder include Commonwealth Scientific and Industrial Research Organisation & Technical University of Denmark.
Papers
More filters
Proceedings ArticleDOI
Sparse multiscale gaussian process regression
TL;DR: This work performs gradient based optimisation of the marginal likelihood, which costs O(m2n) time, and compares the method to various other sparse g.p. methods, which outperforms the other methods, particularly for the case of very few basis functions, i.e. a very high sparsity ratio.
Journal ArticleDOI
Implicit Surface Modelling with a Globally Regularised Basis of Compact Support
TL;DR: This work considers the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors.
Proceedings ArticleDOI
Implicit surface modelling as an eigenvalue problem
TL;DR: The method solves a non-convex optimisation problem in the embedding function that defines the implicit by way of its zero level set by assuming that the solution is a mixture of radial basis functions of varying widths.
Proceedings Article
Diffeomorphic Dimensionality Reduction
TL;DR: This paper argues that constraining the mapping between the high and low dimensional spaces to be a diffeomorphism is a natural way of ensuring that pairwise distances are approximately preserved and develops an algorithm which diffeomorphicically maps the data near to a lower dimensional subspace and then projects onto that subspace.
Proceedings ArticleDOI
Efficient Non-parametric Bayesian Hawkes Processes.
Rui Zhang,Rui Zhang,Christian Walder,Christian Walder,Marian-Andrei Rizoiu,Lexing Xie,Lexing Xie +6 more
TL;DR: An efficient nonparametric Bayesian estimation of the kernel function of Hawkes processes is developed and it is shown that on diffusions related to online videos, the learned kernels reflect the perceived longevity for different content types such as music or pets videos.