Sijia Liu

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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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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. ∙ 30 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks

Recent studies have shown that adversarial examples in state-of-the-art ...
05/30/2018 ∙ by Chun-Chen Tu, et al. ∙ 6 ∙ 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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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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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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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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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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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 Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications

The von Neumann graph entropy (VNGE) facilitates the measure of informat...
05/30/2018 ∙ by Pin-Yu Chen, et al. ∙ 4 ∙ 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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Semiblind subgraph reconstruction in Gaussian graphical models

Consider a social network where only a few nodes (agents) have meaningfu...
11/15/2017 ∙ by Tianpei Xie, et al. ∙ 0 ∙ share

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Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications

In this paper, we design and analyze a new zeroth-order online algorithm...
10/21/2017 ∙ by Sijia Liu, et al. ∙ 0 ∙ share

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A Memristor-Based Optimization Framework for AI Applications

Memristors have recently received significant attention as ubiquitous de...
10/18/2017 ∙ by Sijia Liu, et al. ∙ 0 ∙ share

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Bias-Variance Tradeoff of Graph Laplacian Regularizer

This paper presents a bias-variance tradeoff of graph Laplacian regulari...
06/02/2017 ∙ by Pin-Yu Chen, et al. ∙ 0 ∙ share

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Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity

We consider the problem of accelerating distributed optimization in mult...
04/18/2017 ∙ by Sijia Liu, et al. ∙ 0 ∙ share

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A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks

In this paper, we propose an optimization-based sparse learning approach...
12/12/2017 ∙ by Farshad Harirchi, et al. ∙ 0 ∙ share

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Model Reduction in Chemical Reaction Networks: A Data-Driven Sparse-Learning Approach

The reduction of large kinetic mechanisms is a crucial step for fluid dy...
12/18/2017 ∙ by Omar A. Khalil, et al. ∙ 0 ∙ share

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A Comparison of Word Embeddings for the Biomedical Natural Language Processing

Neural word embeddings have been widely used in biomedical Natural Langu...
02/01/2018 ∙ by Yanshan Wang, et al. ∙ 0 ∙ share

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An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks

Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
04/09/2018 ∙ by Pu Zhao, et al. ∙ 0 ∙ share

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Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization

As application demands for zeroth-order (gradient-free) optimization acc...
05/25/2018 ∙ by Sijia Liu, et al. ∙ 0 ∙ 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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A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction

Objective: To automatically create large labeled training datasets and r...
04/20/2018 ∙ by Yanshan Wang, et al. ∙ 0 ∙ 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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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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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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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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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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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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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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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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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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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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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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Sijia Liu

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