activity
20102025
most citedScalable Gradients for Stochastic Differential Equations

134 citations · 185 across the 22 of their papers we have counts for

collaborators

26 papers

math.ST2025

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…

math.PR2025

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…

math.PR2024★ 2 cited

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…

math.AP2024

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…

cs.IT2023★ 1 cited

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…

math.PR2023★ 2 cited

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…

math.OC2022

Conformal Mirror Descent with Logarithmic Divergences

Amanjit Singh Kainth, Ting-Kam Leonard Wong, Frank Rudzicz

The logarithmic divergence is an extension of the Bregman divergence motivated by optimal transport and a generalized convex duality, and satisfies many remarkable properties. Usin…

math.PR2021★ 1 cited

Tsallis and Rényi deformations linked via a new -duality

Ting-Kam Leonard Wong, Jun Zhang

Tsallis and Rényi entropies, which are monotone transformations of each other, are deformations of the celebrated Shannon entropy. Maximization of these deformed entropies, under s…

math.DG2021★ 1 cited

Projections with logarithmic divergences

Zhixu Tao, Ting-Kam Leonard Wong

In information geometry, generalized exponential families and statistical manifolds with curvature are under active investigation in recent years. In this paper we consider the sta…

q-fin.PM2021

Functional portfolio optimization in stochastic portfolio theory

Steven Campbell, Ting-Kam Leonard Wong

In this paper we develop a concrete and fully implementable approach to the optimization of functionally generated portfolios in stochastic portfolio theory. The main idea is to op…

cs.LG2020★ 134 cited

Scalable Gradients for Stochastic Differential Equations

Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen +1

The adjoint sensitivity method scalably computes gradients of solutions to ordinary differential equations. We generalize this method to stochastic differential equations, allowing…

math.PR2019

Random concave functions

Peter Baxendale, Ting-Kam Leonard Wong

Spaces of convex and concave functions appear naturally in theory and applications. For example, convex regression and log-concave density estimation are important topics in nonpar…

math.DG2019

Logarithmic divergences: geometry and interpretation of curvature

Ting-Kam Leonard Wong, Jiaowen Yang

We study the logarithmic -divergence which extrapolates the Bregman divergence and corresponds to solutions to novel optimal transport problems. We show that this logarith…

q-fin.MF2018

Time-consistent conditional expectation under probability distortion

Jin Ma, Ting-Kam Leonard Wong, Jianfeng Zhang

We introduce a new notion of conditional nonlinear expectation under probability distortion. Such a distorted nonlinear expectation is not sub-additive in general, so it is beyond…

math.PR2018

Multiplicative Schrödinger problem and the Dirichlet transport

Soumik Pal, Ting-Kam Leonard Wong

We consider an optimal transport problem on the unit simplex whose solutions are given by gradients of exponentially concave functions and prove two main results. First, we show th…

math.PR2018

Random walks and induced Dirichlet forms on compact spaces of homogeneous type

Shi-Lei Kong, Ka-Sing Lau, Ting-Kam Leonard Wong

We extend our study of random walks and induced Dirichlet forms on self-similar sets [arXiv:1604.05440, 1612.01708] to compact spaces of homogeneous type . A successive p…

math.PR2017

Logarithmic divergences from optimal transport and Rényi geometry

Ting-Kam Leonard Wong

Divergences, also known as contrast functions, are distance-like quantities defined on manifolds of non-negative or probability measures. Using the duality in optimal transport, we…

q-fin.MF2017★ 2 cited

On portfolios generated by optimal transport

Ting-Kam Leonard Wong

First introduced by Fernholz in stochastic portfolio theory, functionally generated portfolio allows its investment performance to be attributed to directly observable and easily i…

q-fin.MF2016★ 3 cited

Cover's universal portfolio, stochastic portfolio theory and the numeraire portfolio

Christa Cuchiero, Walter Schachermayer, Ting-Kam Leonard Wong

Cover's celebrated theorem states that the long run yield of a properly chosen "universal" portfolio is as good as the long run yield of the best retrospectively chosen constant re…

math.PR2016

Exponentially concave functions and a new information geometry

Soumik Pal, Ting-Kam Leonard Wong

A function is exponentially concave if its exponential is concave. We consider exponentially concave functions on the unit simplex. In a previous paper we showed that gradient maps…

math.PR2016★ 18 cited

Random walks and induced Dirichlet forms on self-similar sets

Shi-Lei Kong, Ka-Sing Lau, Ting-Kam Leonard Wong

Let be a self-similar set satisfying the open set condition. Following Kaimanovich's elegant idea, it has been proved that on the symbolic space of a natural augmented…

q-fin.PM2015★ 10 cited

Universal portfolios in stochastic portfolio theory

Ting-Kam Leonard Wong

Consider a family of portfolio strategies with the aim of achieving the asymptotic growth rate of the best one. The idea behind Cover's universal portfolio is to build a wealth-wei…

q-fin.PM2014★ 1 cited

Optimization of relative arbitrage

Ting-Kam Leonard Wong

In stochastic portfolio theory, a relative arbitrage is an equity portfolio which is guaranteed to outperform a benchmark portfolio over a finite horizon. When the market is divers…

q-fin.PM2014

The geometry of relative arbitrage

Soumik Pal, Ting-Kam Leonard Wong

Consider an equity market with stocks. The vector of proportions of the total market capitalizations that belong to each stock is called the market weight. The market weight de…