Exponentially concave functions and a new information geometry. - Soumik Pal; Ting-Kam Leonard Wong (CoRR, 2016)

Logarithmic divergences from optimal transport and Rényi geometry. - Ting-Kam Leonard Wong (CoRR, 2017)

Scalable Gradients and Variational Inference for Stochastic Differential Equations. - Xuechen Li 005; Ting-Kam Leonard Wong; Ricky T. Q. Chen; David Duvenaud (AABI, 2019)

Logarithmic Divergences: Geometry and Interpretation of Curvature. - Ting-Kam Leonard Wong; Jiaowen Yang (GSI, 2019)

Scalable Gradients for Stochastic Differential Equations. - Xuechen Li 005; Ting-Kam Leonard Wong; Ricky T. Q. Chen; David Duvenaud (AISTATS, 2020)

Projections with Logarithmic Divergences. - Zhixu Tao; Ting-Kam Leonard Wong (GSI, 2021)

Time-Consistent Conditional Expectation Under Probability Distortion. - Jin Ma; Ting-Kam Leonard Wong; Jianfeng Zhang (Math. Oper. Res., 2021)

λ-Deformation: A Canonical Framework for Statistical Manifolds of Constant Curvature. - Jun Zhang 009; Ting-Kam Leonard Wong (Entropy, 2022)

Functional Portfolio Optimization in Stochastic Portfolio Theory. - Steven Campbell; Ting-Kam Leonard Wong (SIAM J. Financial Math., 2022)

Tsallis and Rényi Deformations Linked via a New λ-Duality. - Ting-Kam Leonard Wong; Jun Zhang 009 (IEEE Trans. Inf. Theory, 2022)

Information Geometry for the Working Information Theorist. - Kumar Vijay Mishra; M. Ashok Kumar; Ting-Kam Leonard Wong (CoRR, 2023)

Efficient Convex PCA with Applications to Wasserstein GPCA and Ranked Data. - Steven Campbell; Ting-Kam Leonard Wong (J. Comput. Graph. Stat., 2025)