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Andrew M. Stuart

10 papers hereh-index 471 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author4
  • last author5

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • stat.ML4
  • math.NA2
same name
  • Andrew M. Stuart — 12 papers, h 13
  • Andrew M. Stuart — 9 papers, h 5
  • Andrew M. Stuart — 4 papers, h 3
  • Andrew M. Stuart — 4 papers, h 3
  • Andrew M. Stuart — 2 papers, h 4
  • Andrew M. Stuart — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedLearning Optimal Filters Using Variational Inference

2 citations · 3 across the 9 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

stat.ML2025

A Mathematical Perspective On Contrastive Learning

Ricardo Baptista, Andrew M. Stuart, Son Tran

Multimodal contrastive learning is a methodology for linking different data modalities; the canonical example is linking image and text data. The methodology is typically framed as…

stat.ML2025

Learning Enhanced Ensemble Filters

Eviatar Bach, Ricardo Baptista, Edoardo Calvello +2

The filtering distribution in hidden Markov models evolves according to the law of a mean-field model in state-observation space. The ensemble Kalman filter (EnKF) approximates thi…

math.NA2025

Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks

Ricardo Baptista, Edoardo Calvello, Matthieu Darcy +3

We consider the use of Gaussian Processes (GPs) or Neural Networks (NNs) to numerically approximate the solutions to nonlinear partial differential equations (PDEs) with rough forc…

cs.LG2025

Memorization and Regularization in Generative Diffusion Models

Ricardo Baptista, Agnimitra Dasgupta, Nikola B. Kovachki +2

Diffusion models have emerged as a powerful framework for generative modeling. At the heart of the methodology is score matching: learning gradients of families of log-densities fo…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.