2 citations · 3 across the 6 of their papers we have counts for
15 papers
Maximum likelihood estimation for the -exponential family
Xiwei Tian, Ting-Kam Leonard Wong, Jiaowen Yang +1
The -exponential family generalizes the standard exponential family via a generalized convex duality motivated by optimal transport. It is the constant-curvature analogue of the…
On the Wasserstein alignment problem
Soumik Pal, Bodhisattva Sen, Ting-Kam Leonard Wong
Suppose we are given two metric spaces and a family of continuous transformations from one to the other. Given a probability distribution on each of these two spaces -- namely the…
Adapted optimal transport between Gaussian processes in discrete time
Madhu Gunasingam, Ting-Kam Leonard Wong
We derive explicitly the adapted -Wasserstein distance between non-degenerate Gaussian distributions on and characterize the optimal bicausal coupling(s). This le…
JKO schemes with general transport costs
Cale Rankin, Ting-Kam Leonard Wong
We modify the JKO scheme, which is a time discretization of Wasserstein gradient flows, by replacing the Wasserstein distance with more general transport costs on manifolds. We sho…
Information Geometry for the Working Information Theorist
Kumar Vijay Mishra, M. Ashok Kumar, Ting-Kam Leonard Wong
Information geometry is a study of statistical manifolds, that is, spaces of probability distributions from a geometric perspective. Its classical information-theoretic application…
Bregman-Wasserstein divergence: geometry and applications
Amanjit Singh Kainth, Cale Rankin, Ting-Kam Leonard Wong
The Bregman-Wasserstein divergence is the optimal transport cost when the underlying cost function is given by a Bregman divergence, and arises naturally in fields such as statisti…