6 accepted papers
This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient testing frameworks as a principled foundation for ensuring reliability and safety in real-world deployment. A recent review found that only 9\% of FDA-regi…
In communication systems, the challenge of ensuring accurate data transmission across noisy channels remains paramount. While semantic communication shows potential in improving image transmission and reconstruction, existing methods still suffer from perceptual quality degradation in high-noise env…
Gaussian graphical models have emerged as a powerful tool for modeling and understanding multivariate data across various domains. In this paper, we consider the problem of change localization in the Gaussian graphical model, where it is known that a change has occurred in the underlying graph struc…
Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis). We consider a new setting for this problem where sample features are easily measured whereas sample labels are unknown and costly…
Accurately and efficiently characterizing the decision boundary of classifiers is important for problems related to model selection and meta-learning. Inspired by topological data analysis, the characterization of decision boundaries using their homology has recently emerged as a general and powerfu…
Regularization is an effective way to promote the generalization performance of machine learning models. In this paper, we focus on label smoothing, a form of output distribution regularization that prevents overfitting of a neural network by softening the ground-truth labels in the training data in…