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.
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.