9 papers
Compatible Likelihoods for Flow Matching on Manifolds
Lucas Ng, Georgios Batzolis, Mark Girolami
On the simplest non-Euclidean manifold, the 1D flat torus, we exhibit a distribution where a recently proposed extension of Riemannian flow matching is globally optimized by a velo…
Multimodal Co-Generation With Continuous Bitstream Diffusion
Georgios Batzolis, Georgios Kontos, Bruno Trentini +4
We show that continuous bitstream diffusion (CoBit), originally developed for language modeling, provides a unified framework for joint and conditional multimodal generation using…
Beyond Unimodal Bases: Pullback Geometry for Multimodal Data
Honglei Brinkmann, Lucas Ng, Georgios Batzolis +3
Data-driven Riemannian geometry provides nonlinear interpolation and geometric representations of high-dimensional data. For these operations to be statistically meaningful, paths…
When Noise Estimation Hides Basis Misspecification in Repeated Bayesian Inverse Problems
Rares Dimitrie Grozavescu, Mark Girolami
Basis-restricted priors in Bayesian inverse problems can lose coverage when the truth has components outside the basis. We show that estimating the observation-noise variance can h…
Generative Priors Conditioned on Natural Language for Bayesian Inversion in PDEs
Pengyu Zhang, Mark Girolami, Arnaud Vadeboncoeur
Inferring quantities of interest (QoI) from data is a central task in Science and Engineering. In such contexts, we often have access to both quantitative data and qualitative data…
CoBit: Language Modeling with Bitstream Diffusion
Georgios Batzolis, Mark Girolami, Luca Ambrogioni
Diffusion language models (DLMs) promise parallel, order-agnostic generation, but on standard benchmarks they have historically lagged behind autoregressive models in sample qualit…
Distributional Inverse Homogenization
Arnaud Vadeboncoeur, Mark Girolami, Kaushik Bhattacharya +1
For many materials, macroscopic mechanical behavior is determined by an intricate microstructure. Understanding the relation between these two scales helps scientists and engineers…
Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach
Rares Grozavescu, Pengyu Zhang, Mark Girolami +1
We investigate the Continuous-Time Koopman Autoencoder (CT-KAE) as a lightweight surrogate model for long-horizon ocean state forecasting in a two-layer quasi-geostrophic (QG) syst…
Hierarchical Inference and Closure Learning via Adaptive Surrogates for ODEs and PDEs
Pengyu Zhang, Arnaud Vadeboncoeur, Alex Glyn-Davies +1
Inverse problems are the task of calibrating models to match data. They play a pivotal role in diverse engineering applications by allowing practitioners to align models with reali…