384 citations · 505 across the 25 of their papers we have counts for
28 papers
Energy Efficiency Maximization for Integrated Sensing and Communication in IoT C-RAN
Le Tung Giang, Nguyen Xuan Tung, Trinh Van Chien +2
Integrated sensing and communication (ISAC) with cloud radio access networks (C-RAN) unifies two foundational technologies for the Internet of Things (IoT), enabling both high-data…
Semi-Supervised Learning on Graphs using Graph Neural Networks
Juntong Chen, Claire Donnat, Olga Klopp +1
Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this ga…
Joint learning of a network of linear dynamical systems via total variation penalization
Claire Donnat, Olga Klopp, Hemant Tyagi
We consider the problem of joint estimation of the parameters of linear dynamical systems, given access to single realizations of their respective trajectories, each of length…
SpeedCP: Fast Kernel-based Conditional Conformal Prediction
Yating Liu, Yeo Jin Jung, Zixuan Wu +2
Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. We build upon the RKHS-based framework of Gibbs et al. (2023), which leve…
Filtering with Confidence: When Data Augmentation Meets Conformal Prediction
Zixuan Wu, So Won Jeong, Yating Liu +2
With promising empirical performance across a wide range of applications, synthetic data augmentation appears a viable solution to data scarcity and the demands of increasingly dat…
Efficient Canonical Correlation Analysis with Sparsity
Zixuan Wu, Elena Tuzhilina, Coralie Rousseau +1
In high-dimensional settings, Canonical Correlation Analysis (CCA) often fails, and existing sparse methods force an untenable choice between computational speed and statistical ri…
LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks
So Won Jeong, Claire Donnat
Graph Neural Networks (GNNs) are increasingly used in conjunction with unsupervised learning techniques to learn powerful node representations, but their deployment is hindered by…
Tensor Topic Modeling Via HOSVD
Yating Liu, Claire Donnat
By representing documents as mixtures of topics, topic modeling has allowed the successful analysis of datasets across a wide spectrum of applications ranging from ecology to genet…
Graph Topic Modeling for Documents with Spatial or Covariate Dependencies
Yeo Jin Jung, Claire Donnat
We address the challenge of incorporating document-level metadata into topic modeling to improve topic mixture estimation. To overcome the computational complexity and lack of theo…
Understanding the Effect of GCN Convolutions in Regression Tasks
Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat +1
Graph Convolutional Networks (GCNs) have become a pivotal method in machine learning for modeling functions over graphs. Despite their widespread success across various application…
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks
Jihee You, So Won Jeong, Claire Donnat
With the proliferation of Graph Neural Network (GNN) methods stemming from contrastive learning, unsupervised node representation learning for graph data is rapidly gaining tractio…
Canonical Correlation Analysis as Reduced Rank Regression in High Dimensions
Claire Donnat, Elena Tuzhilina
Canonical correlation analysis is a widespread technique for discovering linear relationships between two sets of variables. In high dimensions, however, standard estimates of the…
One-Bit Total Variation Denoising over Networks with Applications to Partially Observed Epidemics
Claire Donnat, Olga Klopp, Nicolas Verzelen
This paper introduces a novel approach for epidemic nowcasting and forecasting over networks using total variation (TV) denoising, a method inspired by classical signal processing…
Sparse topic modeling via spectral decomposition and thresholding
Huy Tran, Yating Liu, Claire Donnat
The probabilistic Latent Semantic Indexing model assumes that the expectation of the corpus matrix is low-rank and can be written as the product of a topic-word matrix and a word-d…
A Simplified Framework for Contrastive Learning for Node Representations
Ilgee Hong, Huy Tran, Claire Donnat
Contrastive learning has recently established itself as a powerful self-supervised learning framework for extracting rich and versatile data representations. Broadly speaking, cont…
The Generalized Elastic Net for least squares regression with network-aligned signal and correlated design
Huy Tran, Sansen Wei, Claire Donnat
We propose a novel -penalty, which we refer to as the Generalized Elastic Net, for regression problems where the feature vectors are indexed by vertices of a given g…
Tuning the Geometry of Graph Neural Networks
Sowon Jeong, Claire Donnat
By recursively summing node features over entire neighborhoods, spatial graph convolution operators have been heralded as key to the success of Graph Neural Networks (GNNs). Yet, d…
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
Adele Myers, Saiteja Utpala, Shubham Talbar +25
This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop ``Geometric and Topological Representation Learn…
Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy
Claire Donnat, Axel Levy, Frederic Poitevin +2
Recent breakthroughs in high-resolution imaging of biomolecules in solution with cryo-electron microscopy (cryo-EM) have unlocked new doors for the reconstruction of molecular volu…
Statistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic
Saskia Comess, Hannah Wang, Susan Holmes +1
Pooled testing offers an efficient solution to the unprecedented testing demands of the COVID-19 pandemic, although with potentially lower sensitivity and increased costs to implem…
A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses
Claire Donnat, Nina Miolane, Freddy Bunbury +1
Computer-Aided Diagnosis has shown stellar performance in providing accurate medical diagnoses across multiple testing modalities (medical images, electrophysiological signals, etc…
Geomstats: A Python Package for Riemannian Geometry in Machine Learning
Nina Miolane, Alice Le Brigant, Johan Mathe +16
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matri…
Modeling the Heterogeneity in COVID-19's Reproductive Number and its Impact on Predictive Scenarios
Claire Donnat, Susan Holmes
The correct evaluation of the reproductive number for COVID-19 -- which characterizes the average number of secondary cases generated by each typical primary case -- is central…
Convex Hierarchical Clustering for Graph-Structured Data
Claire Donnat, Susan Holmes
Convex clustering is a recent stable alternative to hierarchical clustering. It formulates the recovery of progressively coalescing clusters as a regularized convex problem. While…