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  1. Beygelzimer A ; Dauphin Y ; Liang P ; Wortman Vaughan J (Ed.)
  2. Ranzato, M. ; Beygelzimer, A. ; Dauphin, Y ; Liang, P. S. ; Wortman Vaughan, J. (Ed.)
    Adversarial examples are a widely studied phenomenon in machine learning models. While most of the attention has been focused on neural networks, other practical models also suffer from this issue. In this work, we propose an algorithm for evaluating the adversarial robustness of k-nearest neighbor classification, i.e., finding a minimum-norm adversarial example. Diverging from previous proposals, we propose the first geometric approach by performing a search that expands outwards from a given input point. On a high level, the search radius expands to the nearby higher-order Voronoi cells until we find a cell that classifies differently from the input point. To scale the algorithm to a large k, we introduce approximation steps that find perturbation with smaller norm, compared to the baselines, in a variety of datasets. Furthermore, we analyze the structural properties of a dataset where our approach outperforms the competition. 
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  3. Ranzato, M ; Beygelzimer, A ; Dauphin, Y ; Liang, P. S. ; Wortman Vaughan, J (Ed.)
  4. Ranzato, M ; Beygelzimer, A ; Dauphin, Y ; Liang, P. S. ; Wortman Vaughan, J (Ed.)
  5. Ranzato, M ; Beygelzimer, A ; Dauphin, Y ; Liang, P. S. ; Wortman Vaughan, J (Ed.)
  6. Ranzato, M. ; Beygelzimer, A. ; Dauphin, Y ; Liang, P. S. ; Vaughan, J. W. (Ed.)
    Attention maps are popular tools for explaining the decisions of convolutional neural networks (CNNs) for image classification. Typically, for each image of interest, a single attention map is produced, which assigns weights to pixels based on their importance to the classification. We argue that a single attention map provides an incomplete understanding since there are often many other maps that explain a classification equally well. In this paper, we propose to utilize a beam search algorithm to systematically search for multiple explanations for each image. Results show that there are indeed multiple relatively localized explanations for many images. However, naively showing multiple explanations to users can be overwhelming and does not reveal their common and distinct structures. We introduce structured attention graphs (SAGs), which compactly represent sets of attention maps for an image by visualizing how different combinations of image regions impact the confidence of a classifier. An approach to computing a compact and representative SAG for visualization is proposed via diverse sampling. We conduct a user study comparing the use of SAGs to traditional attention maps for answering comparative counterfactual questions about image classifications. Our results show that the users are significantly more accurate when presented with SAGs compared to standard attention map baselines. 
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  7. Ranzato, M. ; Beygelzimer, A. ; Dauphin, Y. ; Liang, P. S. ; Wortman Vaughan, J. (Ed.)
    Bootstrapping has been a primary tool for ensemble and uncertainty quantification in machine learning and statistics. However, due to its nature of multiple training and resampling, bootstrapping deep neural networks is computationally burdensome; hence it has difficulties in practical application to the uncertainty estimation and related tasks. To overcome this computational bottleneck, we propose a novel approach called Neural Bootstrapper (NeuBoots), which learns to generate bootstrapped neural networks through single model training. NeuBoots injects the bootstrap weights into the high-level feature layers of the backbone network and outputs the bootstrapped predictions of the target, without additional parameters and the repetitive computations from scratch. We apply NeuBoots to various machine learning tasks related to uncertainty quantification, including prediction calibrations in image classification and semantic segmentation, active learning, and detection of out-of-distribution samples. Our empirical results show that NeuBoots outperforms other bagging based methods under a much lower computational cost without losing the validity of bootstrapping. 
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