Selected work

Representative Papers

A large-scale dataset of Android applications and their SDK dependencies

Jul 02, 2026

This study addresses the scarcity of large-scale, reproducible, fine-grained data on third-party SDK dependencies in mobile applications, which hinders research into technical ecosystems and privacy infrastructures. The authors construct a public dataset comprising 334,719 app-version observations by combining static APK analysis, code-signing matching, and an automated processing pipeline, leveraging AndroZoo and Exodus Privacy rules to achieve code-level SDK identification. Covering nearly 100,000 distinct applications and 246 SDKs, the dataset enables the construction of an app–SDK bipartite network and maps SDKs to their operating companies, thereby revealing upstream technological control structures. This resource provides a reusable infrastructure for empirical studies on third-party dependencies and privacy practices in the Android ecosystem.

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A Malliavin-Gamma calculus approach to Score Based Diffusion Generative models for random fields

May 19, 2025

This work addresses the theoretical challenge of extending score-based generative models (SGMs) to infinite-dimensional Hilbert spaces—particularly spherical random fields. Methodologically, it introduces the first infinite-dimensional notion of “score” by combining the Malliavin derivative with the Gamma operator, thereby reformulating both forward noising and reverse denoising via Malliavin–Gamma calculus; employs the Cameron–Martin norm to characterize Fisher information and derives a novel upper bound on entropy convergence in Hilbert space; and designs a Whittle–Matérn-type noise process tailored to spherical domains. Key contributions include: (i) a rigorous theoretical framework for infinite-dimensional SGMs; (ii) a proof that the generalized score coincides with the composition of the Malliavin derivative and conditional expectation; (iii) extension of finite-dimensional entropy convergence analysis to abstract Hilbert spaces; and (iv) instantiation and empirical validation on spherical random fields, establishing a new paradigm for generative modeling on high-dimensional non-Euclidean stochastic domains.

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Hessian stability and convergence rates for entropic and Sinkhorn potentials via semiconcavity

Apr 15, 2025

This work investigates second-order stability of dual potentials and higher-order convergence of the Sinkhorn algorithm in entropy-regularized optimal transport. Addressing the long-standing challenge of unbounded supports, we establish the first quantitative stability bound for the Hessian of entropy potentials. We rigorously derive exponential convergence rates for both the gradient and Hessian of Sinkhorn iterates, with convergence rates exhibiting polynomial dependence on the regularization parameter. Methodologically, we introduce a novel synthesis of semiconcavity analysis, stochastic differential equation representations of Schrödinger bridges, and entropy-optimal transport theory, yielding a unified framework for second-order stability analysis. Our results resolve several open problems concerning higher-order stability and convergence in entropy-regularized OT, providing critical quantitative foundations for both theoretical analysis and practical hyperparameter tuning of the Sinkhorn algorithm.

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Recent publications

Latest Papers

A large-scale dataset of Android applications and their SDK dependencies

Jul 02, 2026

This study addresses the scarcity of large-scale, reproducible, fine-grained data on third-party SDK dependencies in mobile applications, which hinders research into technical ecosystems and privacy infrastructures. The authors construct a public dataset comprising 334,719 app-version observations by combining static APK analysis, code-signing matching, and an automated processing pipeline, leveraging AndroZoo and Exodus Privacy rules to achieve code-level SDK identification. Covering nearly 100,000 distinct applications and 246 SDKs, the dataset enables the construction of an app–SDK bipartite network and maps SDKs to their operating companies, thereby revealing upstream technological control structures. This resource provides a reusable infrastructure for empirical studies on third-party dependencies and privacy practices in the Android ecosystem.

0 citationsRead paper

A Malliavin-Gamma calculus approach to Score Based Diffusion Generative models for random fields

May 19, 2025

This work addresses the theoretical challenge of extending score-based generative models (SGMs) to infinite-dimensional Hilbert spaces—particularly spherical random fields. Methodologically, it introduces the first infinite-dimensional notion of “score” by combining the Malliavin derivative with the Gamma operator, thereby reformulating both forward noising and reverse denoising via Malliavin–Gamma calculus; employs the Cameron–Martin norm to characterize Fisher information and derives a novel upper bound on entropy convergence in Hilbert space; and designs a Whittle–Matérn-type noise process tailored to spherical domains. Key contributions include: (i) a rigorous theoretical framework for infinite-dimensional SGMs; (ii) a proof that the generalized score coincides with the composition of the Malliavin derivative and conditional expectation; (iii) extension of finite-dimensional entropy convergence analysis to abstract Hilbert spaces; and (iv) instantiation and empirical validation on spherical random fields, establishing a new paradigm for generative modeling on high-dimensional non-Euclidean stochastic domains.

0 citationsRead paper

Hessian stability and convergence rates for entropic and Sinkhorn potentials via semiconcavity

Apr 15, 2025

This work investigates second-order stability of dual potentials and higher-order convergence of the Sinkhorn algorithm in entropy-regularized optimal transport. Addressing the long-standing challenge of unbounded supports, we establish the first quantitative stability bound for the Hessian of entropy potentials. We rigorously derive exponential convergence rates for both the gradient and Hessian of Sinkhorn iterates, with convergence rates exhibiting polynomial dependence on the regularization parameter. Methodologically, we introduce a novel synthesis of semiconcavity analysis, stochastic differential equation representations of Schrödinger bridges, and entropy-optimal transport theory, yielding a unified framework for second-order stability analysis. Our results resolve several open problems concerning higher-order stability and convergence in entropy-regularized OT, providing critical quantitative foundations for both theoretical analysis and practical hyperparameter tuning of the Sinkhorn algorithm.

0 citationsRead paper