Search Results for author: Aria Masoomi

Found 14 papers, 4 papers with code

Explanations of Black-Box Models based on Directional Feature Interactions

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.

Geometry of Score Based Generative Models

no code implementations9 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.

Bayesian Inference

Divide and Compose with Score Based Generative Models

no code implementations5 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.

Disentanglement Image Generation +1

Inv-SENnet: Invariant Self Expression Network for clustering under biased data

no code implementations13 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.

Clustering

Boundary-Aware Uncertainty for Feature Attribution Explainers

1 code implementation5 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.

Analyzing Explainer Robustness via Lipschitzness of Prediction Functions

no code implementations24 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.

Deep Layer-wise Networks Have Closed-Form Weights

no code implementations1 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).

Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space

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.

Learning Theory

Deep Bayesian Unsupervised Lifelong Learning

1 code implementation13 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.

Bayesian Inference

Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness

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.

Adversarial Robustness

Instance-wise Feature Grouping

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.

General Classification

Kernel Dependence Network

no code implementations4 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.

Multi-class Classification

Deep Layer-wise Networks Have Closed-Form Weights

no code implementations15 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).

Multi-class Classification

Streaming Adaptive Nonparametric Variational Autoencoder

no code implementations7 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.

Clustering Feature Engineering +1

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