
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several f...
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Evading RealTime Person Detectors by Adversarial Tshirt
It is known that deep neural networks (DNNs) could be vulnerable to adve...
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An InformationTheoretic Perspective on the Relationship Between Fairness and Accuracy
Our goal is to understand the socalled tradeoff between fairness and a...
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Fast Learning of Graph Neural Networks with Guaranteed Generalizability: Onehiddenlayer Case
Although graph neural networks (GNNs) have made great progress recently ...
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An Image Enhancing Patternbased Sparsity for Realtime Inference on Mobile Devices
Weight pruning has been widely acknowledged as a straightforward and eff...
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SignOPT: A QueryEfficient Hardlabel Adversarial Attack
We study the most practical problem setup for evaluating adversarial rob...
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Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Verifying robustness of neural networks given a specified threat model i...
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Structured Adversarial Attack: Towards General Implementation and Better Interpretability
When generating adversarial examples to attack deep neural networks (DNN...
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Progressive Weight Pruning of Deep Neural Networks using ADMM
Deep neural networks (DNNs) although achieving humanlevel performance i...
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Progressive DNN Compression: A Key to Achieve UltraHigh Weight Pruning and Quantization Rates using ADMM
Weight pruning and weight quantization are two important categories of D...
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ZOAdaMM: ZerothOrder Adaptive Momentum Method for BlackBox Optimization
The adaptive momentum method (AdaMM), which uses past gradients to updat...
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CNNCert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Verifying robustness of neural network classifiers has attracted great i...
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Adversarial Robustness: From SelfSupervised PreTraining to FineTuning
Pretrained models from selfsupervision are prevalently used in finetun...
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Proper Network Interpretability Helps Adversarial Robustness in Classification
Recent works have empirically shown that there exist adversarial example...
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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...
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AutoZOOM: Autoencoderbased Zeroth Order Optimization Method for Attacking Blackbox Neural Networks
Recent studies have shown that adversarial examples in stateoftheart ...
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Is Ordered Weighted ℓ_1 Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR
Many stateoftheart machine learning models such as deep neural networ...
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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...
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On the Design of Blackbox Adversarial Examples by Leveraging Gradientfree Optimization and Operator Splitting Method
Robust machine learning is currently one of the most prominent topics wh...
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Beyond Adversarial Training: MinMax Optimization in Adversarial Attack and Defense
The worstcase training principle that minimizes the maximal adversarial...
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Rethinking Randomized Smoothing for Adversarial Robustness
The fragility of modern machine learning models has drawn a considerable...
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Can 3D Adversarial Logos Cloak Humans?
With the trend of adversarial attacks, researchers attempt to fool train...
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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...
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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...
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Semiblind subgraph reconstruction in Gaussian graphical models
Consider a social network where only a few nodes (agents) have meaningfu...
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ZerothOrder Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications
In this paper, we design and analyze a new zerothorder online algorithm...
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A MemristorBased Optimization Framework for AI Applications
Memristors have recently received significant attention as ubiquitous de...
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BiasVariance Tradeoff of Graph Laplacian Regularizer
This paper presents a biasvariance tradeoff of graph Laplacian regulari...
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Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity
We consider the problem of accelerating distributed optimization in mult...
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A DataDriven SparseLearning Approach to Model Reduction in Chemical Reaction Networks
In this paper, we propose an optimizationbased sparse learning approach...
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Model Reduction in Chemical Reaction Networks: A DataDriven SparseLearning Approach
The reduction of large kinetic mechanisms is a crucial step for fluid dy...
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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...
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An ADMMBased Universal Framework for Adversarial Attacks on Deep Neural Networks
Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
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ZerothOrder Stochastic Variance Reduction for Nonconvex Optimization
As application demands for zerothorder (gradientfree) optimization acc...
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Latent heterogeneous multilayer community detection
We propose a method for simultaneously detecting shared and unshared com...
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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...
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On the Convergence of A Class of AdamType Algorithms for NonConvex Optimization
This paper studies a class of adaptive gradient based momentum algorithm...
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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 ...
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Interpreting Adversarial Examples by Activation Promotion and Suppression
It is widely known that convolutional neural networks (CNNs) are vulnera...
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Automated Machine Learning via ADMM
We study the automated machine learning (AutoML) problem of jointly sele...
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MinMax Optimization without Gradients: Convergence and Applications to Adversarial ML
In this paper, we study the problem of constrained robust (minmax) opti...
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A Review of the EndtoEnd Methodologies for Clinical Concept Extraction
Our study provided a review of the concept extraction literature from Ja...
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How can AI Automate EndtoEnd Data Science?
Data science is laborintensive and human experts are scarce but heavily...
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Reweighted Proximal Pruning for LargeScale Language Representation
Recently, pretrained language representation flourishes as the mainstay...
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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...
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SSAuto: A SingleShot, Automatic Structured Weight Pruning Framework of DNNs with UltraHigh Efficiency
Structured weight pruning is a representative model compression techniqu...
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Defending against Backdoor Attack on Deep Neural Networks
Although deep neural networks (DNNs) have achieved a great success in va...
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Solving Constrained CASH Problems with ADMM
The CASH problem has been widely studied in the context of automated con...
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Sijia Liu
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