Sijia Liu

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Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation

Prior works on formalizing explanations of a graph neural network (GNN) ...
03/25/2021 ∙ by Yi Sun, et al. ∙ 10 ∙ share

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Generating Adversarial Computer Programs using Optimized Obfuscations

Machine learning (ML) models that learn and predict properties of comput...
03/18/2021 ∙ by Shashank Srikant, et al. ∙ 12 ∙ share

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On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations

Although deep learning (DL) has received much attention in accelerated M...
02/25/2021 ∙ by Chi Zhang, et al. ∙ 8 ∙ share

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On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning

Model-agnostic meta-learning (MAML) has emerged as one of the most succe...
02/20/2021 ∙ by Ren Wang, et al. ∙ 19 ∙ share

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Lottery Ticket Implies Accuracy Degradation, Is It a Desirable Phenomenon?

In deep model compression, the recent finding "Lottery Ticket Hypothesis...
02/19/2021 ∙ by Ning Liu, et al. ∙ 8 ∙ share

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Fast Training of Provably Robust Neural Networks by SingleProp

Recent works have developed several methods of defending neural networks...
02/01/2021 ∙ by Akhilan Boopathy, et al. ∙ 7 ∙ share

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Self-Progressing Robust Training

Enhancing model robustness under new and even adversarial environments i...
12/22/2020 ∙ by Minhao Cheng, et al. ∙ 9 ∙ share

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Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework

In this work, we focus on the study of stochastic zeroth-order (ZO) opti...
12/21/2020 ∙ by Pranay Sharma, et al. ∙ 17 ∙ share

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The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models

The computer vision world has been re-gaining enthusiasm in various pre-...
12/12/2020 ∙ by Tianlong Chen, et al. ∙ 9 ∙ share

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Training Stronger Baselines for Learning to Optimize

Learning to optimize (L2O) has gained increasing attention since classic...
10/18/2020 ∙ by Tianlong Chen, et al. ∙ 8 ∙ share

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Higher-Order Certification for Randomized Smoothing

Randomized smoothing is a recently proposed defense against adversarial ...
10/13/2020 ∙ by Jeet Mohapatra, et al. ∙ 6 ∙ share

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TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series

Multivariate time series (MTS) data are becoming increasingly ubiquitous...
10/04/2020 ∙ by Yang Jiao, et al. ∙ 19 ∙ share

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Learned Fine-Tuner for Incongruous Few-Shot Learning

Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...
09/29/2020 ∙ by Pu Zhao, et al. ∙ 0 ∙ share

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Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases

When the training data are maliciously tampered, the predictions of the ...
07/31/2020 ∙ by Ren Wang, et al. ∙ 11 ∙ share

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The Lottery Ticket Hypothesis for Pre-trained BERT Networks

In natural language processing (NLP), enormous pre-trained models like B...
07/23/2020 ∙ by Tianlong Chen, et al. ∙ 7 ∙ share

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Proper Network Interpretability Helps Adversarial Robustness in Classification

Recent works have empirically shown that there exist adversarial example...
06/26/2020 ∙ by Akhilan Boopathy, et al. ∙ 11 ∙ share

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Can 3D Adversarial Logos Cloak Humans?

With the trend of adversarial attacks, researchers attempt to fool train...
06/25/2020 ∙ by Tianlong Chen, et al. ∙ 5 ∙ share

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Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case

Although graph neural networks (GNNs) have made great progress recently ...
06/25/2020 ∙ by Shuai Zhang, et al. ∙ 23 ∙ share

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Solving Constrained CASH Problems with ADMM

The CASH problem has been widely studied in the context of automated con...
06/17/2020 ∙ by Parikshit Ram, et al. ∙ 0 ∙ share

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Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

Pretrained models from self-supervision are prevalently used in fine-tun...
03/28/2020 ∙ by Tianlong Chen, et al. ∙ 11 ∙ share

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Rethinking Randomized Smoothing for Adversarial Robustness

The fragility of modern machine learning models has drawn a considerable...
03/02/2020 ∙ by Jeet Mohapatra, et al. ∙ 5 ∙ share

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Defending against Backdoor Attack on Deep Neural Networks

Although deep neural networks (DNNs) have achieved a great success in va...
02/26/2020 ∙ by Hao Cheng, et al. ∙ 0 ∙ share

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Towards an Efficient and General Framework of Robust Training for Graph Neural Networks

Graph Neural Networks (GNNs) have made significant advances on several f...
02/25/2020 ∙ by Kaidi Xu, et al. ∙ 43 ∙ share

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SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency

Structured weight pruning is a representative model compression techniqu...
01/23/2020 ∙ by Zhengang Li, et al. ∙ 0 ∙ share

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An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Weight pruning has been widely acknowledged as a straightforward and eff...
01/20/2020 ∙ by Xiaolong Ma, et al. ∙ 18 ∙ share

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Towards Verifying Robustness of Neural Networks Against Semantic Perturbations

Verifying robustness of neural networks given a specified threat model i...
12/19/2019 ∙ by Jeet Mohapatra, et al. ∙ 17 ∙ share

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Development of Clinical Concept Extraction Applications: A Methodology Review

Our study provided a review of the development of clinical concept extra...
10/24/2019 ∙ by Sunyang Fu, et al. ∙ 0 ∙ share

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A Review of the End-to-End Methodologies for Clinical Concept Extraction

Our study provided a review of the concept extraction literature from Ja...
10/24/2019 ∙ by Sunyang Fu, et al. ∙ 0 ∙ share

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How can AI Automate End-to-End Data Science?

Data science is labor-intensive and human experts are scarce but heavily...
10/22/2019 ∙ by Charu Aggarwal, et al. ∙ 0 ∙ share

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Evading Real-Time Person Detectors by Adversarial T-shirt

It is known that deep neural networks (DNNs) could be vulnerable to adve...
10/18/2019 ∙ by Kaidi Xu, et al. ∙ 41 ∙ share

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An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy

Our goal is to understand the so-called trade-off between fairness and a...
10/17/2019 ∙ by Sanghamitra Dutta, et al. ∙ 29 ∙ share

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ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

The adaptive momentum method (AdaMM), which uses past gradients to updat...
10/15/2019 ∙ by Xiangyi Chen, et al. ∙ 16 ∙ share

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Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML

In this paper, we study the problem of constrained robust (min-max) opti...
09/30/2019 ∙ by Sijia Liu, et al. ∙ 0 ∙ share

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Reweighted Proximal Pruning for Large-Scale Language Representation

Recently, pre-trained language representation flourishes as the mainstay...
09/27/2019 ∙ by Fu-Ming Guo, et al. ∙ 0 ∙ share

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Sign-OPT: A Query-Efficient Hard-label Adversarial Attack

We study the most practical problem setup for evaluating adversarial rob...
09/24/2019 ∙ by Minhao Cheng, et al. ∙ 17 ∙ share

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On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method

Robust machine learning is currently one of the most prominent topics wh...
07/26/2019 ∙ by Pu Zhao, et al. ∙ 6 ∙ share

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Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Graph neural networks (GNNs) which apply the deep neural networks to gra...
06/10/2019 ∙ by Kaidi Xu, et al. ∙ 4 ∙ share

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Beyond Adversarial Training: Min-Max Optimization in Adversarial Attack and Defense

The worst-case training principle that minimizes the maximal adversarial...
06/09/2019 ∙ by Jingkang Wang, et al. ∙ 5 ∙ share

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Automated Machine Learning via ADMM

We study the automated machine learning (AutoML) problem of jointly sele...
05/01/2019 ∙ by Sijia Liu, et al. ∙ 0 ∙ share

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Interpreting Adversarial Examples by Activation Promotion and Suppression

It is widely known that convolutional neural networks (CNNs) are vulnera...
04/03/2019 ∙ by Kaidi Xu, et al. ∙ 0 ∙ share

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Second Rethinking of Network Pruning in the Adversarial Setting

It is well known that deep neural networks (DNNs) are vulnerable to adve...
03/29/2019 ∙ by Shaokai Ye, et al. ∙ 6 ∙ share

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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

Weight pruning and weight quantization are two important categories of D...
03/23/2019 ∙ by Shaokai Ye, et al. ∙ 16 ∙ share

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CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model

Background: Widespread adoption of electronic health records (EHRs) has ...
01/22/2019 ∙ by Sijia Liu, et al. ∙ 0 ∙ share

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CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks

Verifying robustness of neural network classifiers has attracted great i...
11/29/2018 ∙ by Akhilan Boopathy, et al. ∙ 12 ∙ share

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A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM

Many model compression techniques of Deep Neural Networks (DNNs) have be...
11/05/2018 ∙ by Shaokai Ye, et al. ∙ 10 ∙ share

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Progressive Weight Pruning of Deep Neural Networks using ADMM

Deep neural networks (DNNs) although achieving human-level performance i...
10/17/2018 ∙ by Shaokai Ye, et al. ∙ 16 ∙ share

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Is Ordered Weighted ℓ_1 Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR

Many state-of-the-art machine learning models such as deep neural networ...
09/24/2018 ∙ by Pin-Yu Chen, et al. ∙ 6 ∙ share

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On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization

This paper studies a class of adaptive gradient based momentum algorithm...
08/08/2018 ∙ by Xiangyi Chen, et al. ∙ 0 ∙ share

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Structured Adversarial Attack: Towards General Implementation and Better Interpretability

When generating adversarial examples to attack deep neural networks (DNN...
08/05/2018 ∙ by Kaidi Xu, et al. ∙ 16 ∙ share

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Latent heterogeneous multilayer community detection

We propose a method for simultaneously detecting shared and unshared com...
06/16/2018 ∙ by Hafiz Tiomoko Ali, et al. ∙ 0 ∙ share

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Sijia Liu

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