skip to main content
Explore Scholarly Publications and Datasets in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 2

Search for: All records

Award ID contains: 1925101

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ( , SIAM Journal on Mathematics of Data Science)
    Free, publicly-accessible full text available June 1, 2023
  2. ; ; ( , IEEE Statistical Signal Processing Workshop)
    Full Text Available 
  3. ; ; ( , IEEE Transactions on Computational Imaging)
    Full Text Available 
  4. ; ; ; ; ( , IEEE Computer Society Conference on Computer Vision and Pattern Recognition)
    We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search process to maximize inpainting error over these partitions. Our method does not involve training deep networks, is computationally cheap, class-agnostic, and even applicable in isolation to a single unlabeled image. Experiments demonstrate that it achieves a new state-of-the-art in unsupervised segmentation quality, while being substantially fastermore »and more general than competing approaches.« less
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ( , IEEE Transactions on Geoscience and Remote Sensing)
    null (Ed.)
    Full Text Available 
  6. ; ; ; ( , Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ( , International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  8. ; ; ( , IEEE Transactions on Computational Imaging)
    Full Text Available 
  9. ; ; ; ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  10. ; ; ; ( , Journal of machine learning research)
    Detecting when the underlying distribution changes for the observed time series is a fundamental problem arising in a broad spectrum of applications. In this paper, we study multiple change-point localization in the high-dimensional regression setting, which is particularly challenging as no direct observations of the parameter of interest is available. Specifically, we assume we observe {xt,yt}nt=1 where {xt}nt=1 are p-dimensional covariates, {yt}nt=1 are the univariate responses satisfying 𝔼(yt)=x⊤tβ∗t for 1≤t≤n and {β∗t}nt=1 are the unobserved regression coefficients that change over time in a piecewise constant manner. We propose a novel projection-based algorithm, Variance Projected Wild Binary Segmentation~(VPWBS), which transforms themore »original (difficult) problem of change-point detection in p-dimensional regression to a simpler problem of change-point detection in mean of a one-dimensional time series. VPWBS is shown to achieve sharp localization rate Op(1/n) up to a log factor, a significant improvement from the best rate Op(1/n‾√) known in the existing literature for multiple change-point localization in high-dimensional regression. Extensive numerical experiments are conducted to demonstrate the robust and favorable performance of VPWBS over two state-of-the-art algorithms, especially when the size of change in the regression coefficients {β∗t}nt=1 is small.« less
    Accepted Manuscript Full Text Available