publications

2025

1. Connecting Neural Models Latent Geometries with Relative Geodesic Representations

   Hanlin Yu, Berfin Inal, Georgios Arvanitidis, and 3 more authors

   2025

   accepted to NeurIPS 2025
2. Density Ratio Estimation with Conditional Probability Paths

   Hanlin Yu, Arto Klami, Aapo Hyvarinen, and 2 more authors

   In Forty-second International Conference on Machine Learning, 2025
3. Learning geometry and topology via multi-chart flows

   Hanlin Yu, Søren Hauberg, Marcelo Hartmann, and 2 more authors

   2025

   _eprint: 2505.24665

2024

1. Riemannian Laplace Approximation with the Fisher Metric

   Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez, and 2 more authors

   In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, May 2024

   Abs

   Laplace’s method approximates a target density with a Gaussian distribution at its mode. It is computationally efficient and asymptotically exact for Bayesian inference due to the Bernstein-von Mises theorem, but for complex targets and finite-data posteriors it is often too crude an approximation. A recent generalization of the Laplace Approximation transforms the Gaussian approximation according to a chosen Riemannian geometry providing a richer approximation family, while still retaining computational efficiency. However, as shown here, its properties depend heavily on the chosen metric, indeed the metric adopted in previous work results in approximations that are overly narrow as well as being biased even at the limit of infinite data. We correct this shortcoming by developing the approximation family further, deriving two alternative variants that are exact at the limit of infinite data, extending the theoretical analysis of the method, and demonstrating practical improvements in a range of experiments.

2023

1. Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics

   Hanlin Yu, Marcelo Hartmann, Bernardo Williams, and 1 more author

   Transactions on Machine Learning Research, May 2023