1 code implementation • ICLR 2022 • Aria Masoomi, Davin Hill, Zhonghui Xu, Craig P Hersh, Edwin K. Silverman, Peter J. Castaldi, Stratis Ioannidis, Jennifer Dy
As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent.
no code implementations • 9 Feb 2023 • Sandesh Ghimire, Jinyang Liu, Armand Comas, Davin Hill, Aria Masoomi, Octavia Camps, Jennifer Dy
We demonstrate that looking from geometric perspective enables us to answer many of these questions and provide new interpretations to some known results.
no code implementations • 5 Feb 2023 • Sandesh Ghimire, Armand Comas, Davin Hill, Aria Masoomi, Octavia Camps, Jennifer Dy
Towards the direction of having more control over image manipulation and conditional generation, we propose to learn image components in an unsupervised manner so that we can compose those components to generate and manipulate images in informed manner.
no code implementations • 13 Nov 2022 • Ashutosh Singh, Ashish Singh, Aria Masoomi, Tales Imbiriba, Erik Learned-Miller, Deniz Erdogmus
Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well.
1 code implementation • 5 Oct 2022 • Davin Hill, Aria Masoomi, Max Torop, Sandesh Ghimire, Jennifer Dy
In this work we propose the Gaussian Process Explanation UnCertainty (GPEC) framework, which generates a unified uncertainty estimate combining decision boundary-aware uncertainty with explanation function approximation uncertainty.
no code implementations • 24 Jun 2022 • Zulqarnain Khan, Davin Hill, Aria Masoomi, Joshua Bone, Jennifer Dy
We provide lower bound guarantees on the astuteness of a variety of explainers (e. g., SHAP, RISE, CXPlain) given the Lipschitzness of the prediction function.
no code implementations • 1 Feb 2022 • Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy
There is currently a debate within the neuroscience community over the likelihood of the brain performing backpropagation (BP).
no code implementations • NeurIPS 2021 • Sandesh Ghimire, Aria Masoomi, Jennifer Dy
To achieve this objective, we 1) present a novel construction of the discriminator in the Reproducing Kernel Hilbert Space (RKHS), 2) theoretically relate the error probability bound of the KL estimates to the complexity of the discriminator in the RKHS space, 3) present a scalable way to control the complexity (RKHS norm) of the discriminator for a reliable estimation of KL divergence, and 4) prove the consistency of the proposed estimator.
1 code implementation • 13 Jun 2021 • Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer Dy
We develop a fully Bayesian inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, which can progressively discover new clusters without forgetting the past with unlabelled data while learning latent representations.
1 code implementation • NeurIPS 2021 • Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy
We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier.
no code implementations • NeurIPS 2020 • Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy
Moreover, the features that belong to each group, and the important feature groups may vary per sample.
no code implementations • 4 Nov 2020 • Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy
We propose a greedy strategy to spectrally train a deep network for multi-class classification.
no code implementations • 15 Jun 2020 • Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy
There is currently a debate within the neuroscience community over the likelihood of the brain performing backpropagation (BP).
no code implementations • 7 Jun 2019 • Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer G. Dy
We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data.