Geometry and Dynamics for Markov Chain Monte Carlo. - Alessandro Barp; François-Xavier Briol; Anthony D. Kennedy; Mark A. Girolami (CoRR, 2017)

Irreversible Langevin MCMC on Lie Groups. - Alexis Arnaudon; Alessandro Barp; So Takao (GSI, 2019)

Hamiltonian Monte Carlo on Lie Groups and Constrained Mechanics on Homogeneous Manifolds. - Alessandro Barp (GSI, 2019)

Stein Point Markov Chain Monte Carlo. - Wilson Ye Chen; Alessandro Barp; François-Xavier Briol; Jackson Gorham; Mark A. Girolami; Lester W. Mackey; Chris J. Oates (ICML, 2019)

Minimum Stein Discrepancy Estimators. - Alessandro Barp; François-Xavier Briol; Andrew B. Duncan; Mark A. Girolami; Lester W. Mackey (NeurIPS, 2019)

Statistical Inference for Generative Models with Maximum Mean Discrepancy. - François-Xavier Briol; Alessandro Barp; Andrew B. Duncan; Mark A. Girolami (CoRR, 2019)

Geometric Integration of Measure-Preserving Flows for Sampling. - Alessandro Barp (SPIGL, 2020)

Metrizing Weak Convergence with Maximum Mean Discrepancies. - Carl-Johann Simon-Gabriel; Alessandro Barp; Lester Mackey (CoRR, 2020)

Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents. - Alessandro Barp; Lancelot Da Costa; Guilherme França; Karl J. Friston; Mark A. Girolami; Michael I. Jordan; Grigorios A. Pavliotis (CoRR, 2022)

Targeted Separation and Convergence with Kernel Discrepancies. - Alessandro Barp; Carl-Johann Simon-Gabriel; Mark Girolami; Lester Mackey (CoRR, 2022)

Vector-Valued Control Variates. - Zhuo Sun; Alessandro Barp; François-Xavier Briol (ICML, 2023)

Warped geometric information on the optimisation of Euclidean functions. - Marcelo Hartmann; Bernardo Williams; Hanlin Yu; Mark Girolami; Alessandro Barp; Arto Klami (CoRR, 2023)

Improving embedding of graphs with missing data by soft manifolds. - Andrea Marinoni; Pietro Lio'; Alessandro Barp; Christian Jutten; Mark Girolami (CoRR, 2023)

Metrizing Weak Convergence with Maximum Mean Discrepancies. - Carl-Johann Simon-Gabriel; Alessandro Barp; Bernhard Schölkopf; Lester Mackey (J. Mach. Learn. Res., 2023)

Deep Learning as Ricci Flow. - Anthony Baptista; Alessandro Barp; Tapabrata Chakraborti; Chris Harbron; Ben D. MacArthur; Christopher R. S. Banerji (CoRR, 2024)