Publications

Work on statistical learning, networks, and scientific discovery. Peer-reviewed articles and conference papers are listed first, followed by preprints and submitted manuscripts.

Google Scholar

2026 publications

Journal of Machine Learning Research · Accepted, forthcoming · 2026

Tensor Topic Modeling via HOSVD

Yating Liu and Claire Donnat.

Journal of Machine Learning Research, 27(160):1–84 · 2026

Canonical Correlation Analysis as Reduced Rank Regression in High Dimensions

Claire Donnat and Elena Tuzhilina.

arXiv

Electronic Journal of Statistics · 2026

Efficient Canonical Correlation Analysis with Sparsity

Zixuan Wu, Coralie Rousseau, Elena Tuzhilina, and Claire Donnat.

arXiv

International Conference on Machine Learning (ICML) · 2026

SpeedCP: Fast Kernel-based Conditional Conformal Prediction

Yating Liu, Yeo Jin Jung, Zixuan Wu, So Won Jeong, and Claire Donnat.

arXiv

Journal of Machine Learning Research, 27(59):1–76 · 2026

Sparse Topic Modeling via Spectral Decomposition and Thresholding

Huy Tran, Yating Liu, and Claire Donnat.

arXiv

Computational Statistics & Data Analysis, 215 · 2026

Denoising over networks with applications to partially observed epidemics

Claire Donnat, Olga Klopp, and Nicolas Verzelen.

arXiv

2025 publications

Molecular Systems Design & Engineering, 10:345–356 · 2025

Expediting Field-Effect Transistor Chemical Sensor Design with Neuromorphic Spiking Graph Neural Networks

Rodrigo P. Ferreira, Rui Ding, Fengxue Zhang, Haihui Pu, Claire Donnat, Yuxin Chen, and Junhong Chen.

AISTATS, Proceedings of Machine Learning Research, 258:4573–4581 · 2025

Understanding the Effect of GCN Convolutions in Regression Tasks

Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat, and Olga Klopp.

arXiv

IEEE Transactions on Signal and Information Processing over Networks, 11:1459–1472 · 2025

The Generalized Elastic Net for Least Squares Regression With Network-Aligned Signal and Correlated Design

Huy Tran, Sansen Wei, and Claire Donnat.

Earlier publications

Asilomar Conference on Signals, Systems, and Computers, pp. 573–577 · 2023

A Simplified Framework for Contrastive Learning for Node Representations

Ilgee Hong, Huy Tran, and Claire Donnat.

Uncertainty in Artificial Intelligence (UAI), PMLR 216:539–548 · 2023

Studying the Effect of GNN Spatial Convolutions On The Embedding Space’s Geometry

Claire Donnat and So Won Jeong.

Journal of Structural Biology, 214(4):107920 · 2022

Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy

Claire Donnat, Axel Levy, Frédéric Poitevin, Ellen D. Zhong, and Nina Miolane.

arXiv

Statistical Science, 37(2):229–250 · 2022

Statistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic

Saskia Comess, Hannah Wang, Susan Holmes, and Claire Donnat.

arXiv

JMIR Public Health and Surveillance, 7(12):e30648 · 2021

Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach

Claire Donnat, Freddy Bunbury, Jack Kreindler, David Liu, Filippos T. Filippidis, Tonu Esko, Austen El-Osta, and Matthew Harris.

Journal of Applied Statistics · Published online 2021; volume 50(11–12):2518–2546, 2023

Modeling the Heterogeneity in COVID-19’s Reproductive Number and Its Impact on Predictive Scenarios

Claire Donnat and Susan Holmes.

arXiv

Machine Learning for Health, PMLR 136:53–84 · 2020

A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses

Claire Donnat, Nina Miolane, Freddy Bunbury, and Jack Kreindler.

Journal of Machine Learning Research, 21(223):1–9 · 2020

Geomstats: A Python Package for Riemannian Geometry in Machine Learning

Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, and Xavier Pennec.

Nature · 2020

The Annals of Applied Statistics, 12(2):971–1012 · 2018

Tracking Network Dynamics: A Survey Using Graph Distances

Claire Donnat and Susan Holmes.

arXiv

Asilomar Conference on Signals, Systems, and Computers, pp. 1014–1018 · 2016

A Divide-and-Conquer Framework for Large-Scale Subspace Clustering

Chong You, Claire Donnat, Daniel P. Robinson, and René Vidal.

Preprints & manuscripts

Manuscript · Submitted to NeurIPS 2026

SAFE-DRIFT: Data Selection for Supervised Fine-Tuning with Controllable Off-Target Drifts

Yeo Jin Jung, Yating Liu, Lalchland Pandia, and Claire Donnat.

Manuscript · Submitted to NeurIPS 2026

Bias, Measurement Error, and Double-Dipping: When Can GNN Convolutions Help Brain Connectome Prediction?

Tommaso Castellani, Jiaqi Li, and Claire Donnat.

bioRxiv · 2026

Topic Modeling Reveals Thermally Partitioned and Taxonomically Distinct Microbial Subcommunities across Prokaryotes and Phytoplankton in the Laurentian Great Lakes

María D. Hernández Limón, Claire Donnat, Freddy Bunbury, and Maureen L. Coleman.

arXiv · 2026 · Under review at JASA

Publications

2026 publications

Tensor Topic Modeling via HOSVD

Canonical Correlation Analysis as Reduced Rank Regression in High Dimensions

Efficient Canonical Correlation Analysis with Sparsity

SpeedCP: Fast Kernel-based Conditional Conformal Prediction

Sparse Topic Modeling via Spectral Decomposition and Thresholding

Denoising over networks with applications to partially observed epidemics

2025 publications

Expediting Field-Effect Transistor Chemical Sensor Design with Neuromorphic Spiking Graph Neural Networks

Understanding the Effect of GCN Convolutions in Regression Tasks

The Generalized Elastic Net for Least Squares Regression With Network-Aligned Signal and Correlated Design

Earlier publications

A Simplified Framework for Contrastive Learning for Node Representations

Studying the Effect of GNN Spatial Convolutions On The Embedding Space’s Geometry

Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy

Statistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic

Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach

Modeling the Heterogeneity in COVID-19’s Reproductive Number and Its Impact on Predictive Scenarios

A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses

Geomstats: A Python Package for Riemannian Geometry in Machine Learning

Variability in the Analysis of a Single Neuroimaging Dataset by Many Teams

Convex Hierarchical Clustering for Graph-Structured Data

Learning Structural Node Embeddings via Diffusion Wavelets

Tracking Network Dynamics: A Survey Using Graph Distances

A Divide-and-Conquer Framework for Large-Scale Subspace Clustering

Preprints & manuscripts

SAFE-DRIFT: Data Selection for Supervised Fine-Tuning with Controllable Off-Target Drifts

Bias, Measurement Error, and Double-Dipping: When Can GNN Convolutions Help Brain Connectome Prediction?

Topic Modeling Reveals Thermally Partitioned and Taxonomically Distinct Microbial Subcommunities across Prokaryotes and Phytoplankton in the Laurentian Great Lakes

Semi-Supervised Learning on Graphs using Graph Neural Networks

Joint learning of a network of linear dynamical systems via total variation penalization

Filtering with Confidence: When Data Augmentation Meets Conformal Prediction

LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks

Graph Topic Modeling for Documents with Spatial or Covariate Dependencies

GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks

Constrained Bayesian ICA for Brain Connectome Inference