
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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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
As artificial intelligence and machine learning algorithms make further ...
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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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Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning
Networks are fundamental building blocks for representing data, and comp...
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Towards QueryEfficient BlackBox Adversary with ZerothOrder Natural Gradient Descent
Despite the great achievements of the modern deep neural networks (DNNs)...
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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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Toward A Neuroinspired Creative Decoder
Creativity, a process that generates novel and valuable ideas, involves ...
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Enhanced Adversarial StrategicallyTimed Attacks against Deep Reinforcement Learning
Recent deep neural networks based techniques, especially those equipped ...
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AdvMS: A Multisource Multicost Defense Against Adversarial Attacks
Designing effective defense against adversarial attacks is a crucial top...
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When Causal Intervention Meets Image Masking and Adversarial Perturbation for Deep Neural Networks
Discovering and exploiting the causality in deep neural networks (DNNs) ...
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Is Robustness the Cost of Accuracy?  A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the longlasting and sole standard for ...
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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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Model Agnostic Contrastive Explanations for Structured Data
Recently, a method [7] was proposed to generate contrastive explanations...
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PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing stateoftheart machine learning mo...
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Generating Contrastive Explanations with Monotonic Attribute Functions
Explaining decisions of deep neural networks is a hot research topic wit...
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QueryEfficient Hardlabel Blackbox Attack:An Optimizationbased Approach
We study the problem of attacking a machine learning model in the hardl...
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Efficient Neural Network Robustness Certification with General Activation Functions
Finding minimum distortion of adversarial examples and thus certifying r...
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Block Switching: A Stochastic Approach for Deep Learning Security
Recent study of adversarial attacks has revealed the vulnerability of mo...
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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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On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces
Recent studies have found that deep learning systems are vulnerable to a...
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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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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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Protecting Neural Networks with Hierarchical Random Switching: Towards Better RobustnessAccuracy Tradeoff for Stochastic Defenses
Despite achieving remarkable success in various domains, recent studies ...
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CAT: Customized Adversarial Training for Improved Robustness
Adversarial training has become one of the most effective methods for im...
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Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization
Applying Machine Learning (ML) techniques to design and optimize compute...
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Characterizing Audio Adversarial Examples Using Temporal Dependency
Recent studies have highlighted adversarial examples as a ubiquitous thr...
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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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Revisiting Spectral Graph Clustering with Generative Community Models
The methodology of community detection can be divided into two principle...
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EAD: ElasticNet Attacks to Deep Neural Networks via Adversarial Examples
Recent studies have highlighted the vulnerability of deep neural network...
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ZOO: Zeroth Order Optimization based Blackbox Attacks to Deep Neural Networks without Training Substitute Models
Deep neural networks (DNNs) are one of the most prominent technologies o...
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Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms
Multilayer graphs are commonly used for representing different relations...
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Can GAN Learn Topological Features of a Graph?
This paper is firstline research expanding GANs into graph topology ana...
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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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Multilayer Spectral Graph Clustering via Convex Layer Aggregation
Multilayer graphs are commonly used for representing different relations...
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AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
One of the longstanding problems in spectral graph clustering (SGC) is t...
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ShowandFool: Crafting Adversarial Examples for Neural Image Captioning
Modern neural image captioning systems typically adopt the encoderdecod...
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Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
One of the longstanding open problems in spectral graph clustering (SGC)...
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Multicentrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection
Many modern datasets can be represented as graphs and hence spectral dec...
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Incremental Method for Spectral Clustering of Increasing Orders
The smallest eigenvalues and the associated eigenvectors (i.e., eigenpai...
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When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective
Mobile sensing is an emerging technology that utilizes agentparticipato...
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Phase Transitions in Spectral Community Detection of Large Noisy Networks
In this paper, we study the sensitivity of the spectral clustering based...
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Attacking the Madry Defense Model with L_1based Adversarial Examples
The Madry Lab recently hosted a competition designed to test the robustn...
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PinYu Chen
verfied profile
Research Staff Member, Trusted AI Group
Chief Scientist, RPIIBM AI Research Collaboration
PI, MITIBM Watson AI Lab
IBM Thomas J. Watson Research Center