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05/02/2022
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor
In this paper, we investigate an online prediction strategy named as Dis...
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03/27/2022
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
The lack of adversarial robustness has been recognized as an important i...
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01/31/2022
Can Adversarial Training Be Manipulated By Non-Robust Features?
Adversarial training, originally designed to resist test-time adversaria...
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12/15/2021
On the Convergence and Robustness of Adversarial Training
Improving the robustness of deep neural networks (DNNs) to adversarial e...
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12/10/2021
Federated Two-stage Learning with Sign-based Voting
Federated learning is a distributed machine learning mechanism where loc...
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10/22/2021
How and When Adversarial Robustness Transfers in Knowledge Distillation?
Knowledge distillation (KD) has been widely used in teacher-student trai...
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10/13/2021
Adversarial Attack across Datasets
It has been observed that Deep Neural Networks (DNNs) are vulnerable to ...
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10/04/2021
Trustworthy AI: From Principles to Practices
Fast developing artificial intelligence (AI) technology has enabled vari...
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09/05/2021
Training Meta-Surrogate Model for Transferable Adversarial Attack
We consider adversarial attacks to a black-box model when no queries are...
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06/25/2021
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
Providing privacy protection has been one of the primary motivations of ...
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06/17/2021
Towards Heterogeneous Clients with Elastic Federated Learning
Federated learning involves training machine learning models over device...
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06/07/2021
Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Recently, a new form of online shopping becomes more and more popular, w...
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05/08/2021
A Simple yet Universal Strategy for Online Convex Optimization
Recently, several universal methods have been proposed for online convex...
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03/31/2021
Fast Certified Robust Training via Better Initialization and Shorter Warmup
Recently, bound propagation based certified adversarial defense have bee...
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03/29/2021
On the Adversarial Robustness of Visual Transformers
Following the success in advancing natural language processing and under...
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02/09/2021
Provable Defense Against Delusive Poisoning
Delusive poisoning is a special kind of attack to obstruct learning, whe...
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01/07/2021
Robust Text CAPTCHAs Using Adversarial Examples
CAPTCHA (Completely Automated Public Truing test to tell Computers and H...
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12/17/2020
On the Limitations of Denoising Strategies as Adversarial Defenses
As adversarial attacks against machine learning models have raised incre...
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10/29/2020
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
The general aim of the recommender system is to provide personalized sug...
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06/12/2020
Provably Robust Metric Learning
Metric learning is an important family of algorithms for classification ...
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05/11/2020
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data
Adversarial black-box attacks aim to craft adversarial perturbations by ...
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12/07/2019
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
Recently, practical applications for passenger flow prediction have brou...
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08/16/2019
Symmetric Cross Entropy for Robust Learning with Noisy Labels
Training accurate deep neural networks (DNNs) in the presence of noisy l...
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07/21/2019
Characterizing Attacks on Deep Reinforcement Learning
Deep reinforcement learning (DRL) has achieved great success in various ...
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07/02/2019
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points
Railway points are among the key components of railway infrastructure. A...
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06/16/2019
Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and po...
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06/10/2019
Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation aims to transfer the classifier learned f...
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06/10/2019
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
We study the problem of computing the minimum adversarial perturbation o...
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05/28/2019
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
Recent studies have highlighted that deep neural networks (DNNs) are vul...
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05/01/2019
Inferring the Importance of Product Appearance: A Step Towards the Screenless Revolution
Nowadays, almost all the online orders were placed through screened devi...
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04/24/2019
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
Machine learning has been widely applied to various applications, some o...
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11/27/2018
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Depending on how much information an adversary can access to, adversaria...
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09/14/2018
Random Warping Series: A Random Features Method for Time-Series Embedding
Time series data analytics has been a problem of substantial interests f...
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09/13/2018
Query-Efficient Black-Box Attack by Active Learning
Deep neural network (DNN) as a popular machine learning model is found t...
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09/09/2018
Towards Query Efficient Black-box Attacks: An Input-free Perspective
Recent studies have highlighted that deep neural networks (DNNs) are vul...
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08/20/2018
Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
Although stochastic gradient descent () method and its variants (e.g., s...
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08/05/2018
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the long-lasting and sole standard for ...
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07/18/2018
Defend Deep Neural Networks Against Adversarial Examples via Fixed andDynamic Quantized Activation Functions
Recent studies have shown that deep neural networks (DNNs) are vulnerabl...
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07/12/2018
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
We study the problem of attacking a machine learning model in the hard-l...
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06/27/2018
Matrix Completion from Non-Uniformly Sampled Entries
In this paper, we consider matrix completion from non-uniformly sampled ...
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06/20/2018
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
Multiple kernel learning (MKL) method is generally believed to perform b...
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06/14/2018
Financial Forecasting and Analysis for Low-Wage Workers
Despite the plethora of financial services and products on the market no...
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05/30/2018
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 ...
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05/19/2018
Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains. Compared to many...
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03/03/2018
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
Crafting adversarial examples has become an important technique to evalu...
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02/14/2018
Edge Attention-based Multi-Relational Graph Convolutional Networks
Graph convolutional network (GCN) is generalization of convolutional neu...
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02/13/2018
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
The surging availability of electronic medical records (EHR) leads to in...
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01/31/2018
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
The robustness of neural networks to adversarial examples has received g...
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12/06/2017
Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning
Modern neural image captioning systems typically adopt the encoder-decod...
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09/13/2017
