with Hefin Lambley, Tim Sullivan, Classification of small-ball modes and maximum a posteriori estimators, 2025 [arxiv]
with Vesa Kaarnioja, Claudia Schillings, Yuya Suzuki, Lattice Rules Meet Kernel Cubature , 2025 [arxiv]
with Robert Polzin, Nikolas Nüsken, Péter Koltai, Coherent set identification via direct low rank maximum likelihood estimation, 2025 [JNS] [arxiv]
Deterministic Fokker-Planck Transport - With Applications to Sampling, Variational Inference, Kernel Mean Embeddings & Sequential Monte Carlo, 2024 [arxiv]
Graph convex hull bounds as generalized Jensen inequalities, 2024 [BLMS] [arxiv]
with Tim Sullivan, Transporting Higher-Order Quadrature Rules: Quasi-Monte Carlo Points and Sparse Grids for Mixture Distributions, 2023 [arxiv]
with Philipp Wacker, Maximum a posteriori estimators in ℓ^p are well-defined for diagonal Gaussian priors, 2023 [Inverse Problems] [arxiv]
with Birzhan Ayanbayev, Han Cheng Lie, Tim Sullivan, Γ-convergence of Onsager-Machlup functionals. Part II: Infinite product measures on Banach spaces, 2021 [Inverse Problems] [arxiv]
with Birzhan Ayanbayev, Han Cheng Lie, Tim Sullivan, Γ-convergence of Onsager-Machlup functionals. Part I: With applications to maximum a posteriori estimation in Bayesian inverse problems, 2021 [Inverse Problems] [arxiv]
with Björn Sprungk, Tim Sullivan, The linear conditional expectation in Hilbert space, 2021 [Bernoulli] [arxiv]
with Ingmar Schuster, Markov Chain Importance Sampling - a highly efficient estimator for MCMC, 2021 [JCGS] [arxiv]
with Tim Sullivan, Ingmar Schuster, A Rigorous Theory of Conditional Mean Embeddings, 2020 [SIMODS] [arxiv]
with Alexander Sikorski, Christof Schütte, Susanna Röblitz, Objective Priors in the Empirical Bayes Framework, 2020 [SJOS] [arxiv]
Axiomatic Approach to Variable Kernel Density Estimation, 2018 [arxiv]
Adaptive Convolutions, 2018 [arxiv]
Approximation of PDEs with Underlying Continuity Equations, 2015 [pdf]