Publications

Constrained Bayesian ICA for Brain Connectomics

Published in arXiv (Under submission), 2019

We investigate a constrained Bayesian ICA approach for connectome subnetwork discovery. In comparison to current methods, simultaneously allows (a) the flexible integration of multiple sources of information (fMRI, DTI, anatomical, etc.), (b) an automatic and parameter-free selection of the appropriate sparsity level and number of connected submodules and (c) the provision of estimates on the uncertainty of the recovered interactions.

Recommended citation: Claire Donnat, Leonardo Tozzi and Susan Holmes (2019). "Constrained Bayesian ICA for Brain Connectomics 1." arXiv.

Convex Hierarchical Clustering for Graph-Structured Data

Published in IEEE Transactions on Signal Processing, 2019

We extend the robust hierarchical clustering approach to the analysis of Graph-Structured data. Having defined an appropriate convex objective, the crux of this adaptation lies in our ability to provide: (a) an efficient recovery of the regularization path - which we address through a proximal dual algorithm - and (b) an empirical demonstration of the use of our method.

Recommended citation: Donnat, Claire and Holmes, Susan. (2019). "Convex Hierarchical Clustering for Graph-Structured Data." IEEE Transactions on Signal Processing.

NARPS Analysis

Published in (Under submission), 2019

We participated in the NARPS study, an international initiative to estimate the variability of neuroscientific results across analysis teams. The results are currently in the process of being submitted to Nature.

Geomstats: a Python Package for Riemannian Geometry in Machine Learning

Published in arXiv (Under review), 2018

We introduce geomstats, a Python package for Riemannian modelization and optimization over manifolds. With operations implemented with different computing backends (numpy, tensorflow and keras), geomstats provides a unified framework for Riemannian geometry and facilitates its application in machine learning.

Recommended citation: Miolane, Nina et al. (2018). "Geomstats: a Python Package for Riemannian Geometry in Machine Learning." arXiv. https://arxiv.org/abs/1805.08308

Learning Structural Node Embeddings Via Diffusion Wavelets

Published in The 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 19-23, 2018, London, United Kingdom, 2018

We introduce GraphWave, a method for discovering structural similarities on graphs. In particular, GraphWave represents each node s network neighborhood via a low-dimensional embedding by leveraging heat wavelet diffusion patterns.

Recommended citation: Donnat, Claire. (2018). "Learning Structural Node Embeddings Via Diffusion Wavelets."

Tracking network distances: an overview

Published in Annals of Applied Statistics 12.2 (2018): 971-1012, 2018

In this work, we study distances between sets of aligned graphs. In particular, we try to provide ground and principles for choosing an appropriate distance over another, and highlight these properties on both a real-life neuroscience and microbiome applications, as well as synthetic examples.

Recommended citation: Donnat, Claire and Holmes, Susan (2018). "Tracking network distances: an overview." Annals of Applied Statistics 12.2 (2018): 971-1012.

Large-Scale Subspace Clustering for Computer Vision

Published in Signals, Systems and Computers, 2016 50th Asilomar Conference on, pp. 1014-1018. IEEE, 2016, 2016

Subspace clustering is an unsupervised technique that models the data as a union of low-dimensional subspaces. Here, we propose a divide-and-conquer framework for large-scale subspace clustering, allowing it to scale up to datasets of more than 100,000 points.

Recommended citation: Chong You, Claire Donnat, Daniel P. Robinson, and René Vidal. "Large-Scale Subspace Clustering for Computer Vision." http://ieeexplore.ieee.org/abstract/document/7869521/