Search Results for author: Ho Chung Leon Law

Found 5 papers, 4 papers with code

Hyperparameter Learning via Distributional Transfer

1 code implementation NeurIPS 2019 Ho Chung Leon Law, Peilin Zhao, Lucian Chan, Junzhou Huang, Dino Sejdinovic

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved.

Bayesian Optimisation

A Differentially Private Kernel Two-Sample Test

1 code implementation1 Aug 2018 Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park

As a result, a simple chi-squared test is obtained, where a test statistic depends on a mean and covariance of empirical differences between the samples, which we perturb for a privacy guarantee.

Two-sample testing

Variational Learning on Aggregate Outputs with Gaussian Processes

1 code implementation NeurIPS 2018 Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim CD Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu

While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs.

Gaussian Processes

Bayesian Approaches to Distribution Regression

1 code implementation11 May 2017 Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth Flaxman

Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level.

Testing and Learning on Distributions with Symmetric Noise Invariance

no code implementations NeurIPS 2017 Ho Chung Leon Law, Christopher Yau, Dino Sejdinovic

Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric two-sample testing and learning on distributions.

Two-sample testing

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