12 papers
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…
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…
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, Claire Donnat
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…