b'Jinfeng Yi'

research

∙ 05/02/2022

Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor

In this paper, we investigate an online prediction strategy named as Dis...

0 Lijun Zhang, et al. ∙

research

∙ 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...

2 Yimeng Zhang, et al. ∙

research

∙ 01/31/2022

Can Adversarial Training Be Manipulated By Non-Robust Features?

Adversarial training, originally designed to resist test-time adversaria...

0 Lue Tao, et al. ∙

research

∙ 12/15/2021

On the Convergence and Robustness of Adversarial Training

Improving the robustness of deep neural networks (DNNs) to adversarial e...

8 Yisen Wang, et al. ∙

research

∙ 12/10/2021

Federated Two-stage Learning with Sign-based Voting

Federated learning is a distributed machine learning mechanism where loc...

0 Zichen Ma, et al. ∙

research

∙ 10/22/2021

How and When Adversarial Robustness Transfers in Knowledge Distillation?

Knowledge distillation (KD) has been widely used in teacher-student trai...

0 Rulin Shao, et al. ∙

research

∙ 10/13/2021

Adversarial Attack across Datasets

It has been observed that Deep Neural Networks (DNNs) are vulnerable to ...

0 Yunxiao Qin, et al. ∙

research

∙ 10/04/2021

Trustworthy AI: From Principles to Practices

Fast developing artificial intelligence (AI) technology has enabled vari...

0 Bo Li, et al. ∙

research

∙ 09/05/2021

Training Meta-Surrogate Model for Transferable Adversarial Attack

We consider adversarial attacks to a black-box model when no queries are...

0 Yunxiao Qin, et al. ∙

research

∙ 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 ...

0 Xinwei Zhang, et al. ∙

research

∙ 06/17/2021

Towards Heterogeneous Clients with Elastic Federated Learning

Federated learning involves training machine learning models over device...

0 Zichen Ma, et al. ∙

research

∙ 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...

8 Sanshi Yu, et al. ∙

research

∙ 05/08/2021

A Simple yet Universal Strategy for Online Convex Optimization

Recently, several universal methods have been proposed for online convex...

0 Lijun Zhang, et al. ∙

research

∙ 03/31/2021

Fast Certified Robust Training via Better Initialization and Shorter Warmup

Recently, bound propagation based certified adversarial defense have bee...

0 Zhouxing Shi, et al. ∙

research

∙ 03/29/2021

On the Adversarial Robustness of Visual Transformers

Following the success in advancing natural language processing and under...

0 Rulin Shao, et al. ∙

research

∙ 02/09/2021

Provable Defense Against Delusive Poisoning

Delusive poisoning is a special kind of attack to obstruct learning, whe...

5 Lue Tao, et al. ∙

research

∙ 01/07/2021

Robust Text CAPTCHAs Using Adversarial Examples

CAPTCHA (Completely Automated Public Truing test to tell Computers and H...

0 Rulin Shao, et al. ∙

research

∙ 12/17/2020

On the Limitations of Denoising Strategies as Adversarial Defenses

As adversarial attacks against machine learning models have raised incre...

0 Zhonghan Niu, et al. ∙

research

∙ 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...

0 Tianxin Wei, et al. ∙

research

∙ 06/12/2020

Provably Robust Metric Learning

Metric learning is an important family of algorithms for classification ...

0 Lu Wang, et al. ∙

research

∙ 05/11/2020

Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data

Adversarial black-box attacks aim to craft adversarial perturbations by ...

0 Lu Wang, et al. ∙

research

∙ 12/07/2019

Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Recently, practical applications for passenger flow prediction have brou...

24 Yongshun Gong, et al. ∙

research

∙ 08/16/2019

Symmetric Cross Entropy for Robust Learning with Noisy Labels

Training accurate deep neural networks (DNNs) in the presence of noisy l...

0 Yisen Wang, et al. ∙

research

∙ 07/21/2019

Characterizing Attacks on Deep Reinforcement Learning

Deep reinforcement learning (DRL) has achieved great success in various ...

4 Chaowei Xiao, et al. ∙

research

∙ 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...

3 Zhibin Li, et al. ∙

research

∙ 06/16/2019

Defending Against Adversarial Attacks Using Random Forests

As deep neural networks (DNNs) have become increasingly important and po...

1 Yifan Ding, et al. ∙

research

∙ 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...

5 Dong-Dong Chen, et al. ∙

research

∙ 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...

0 Lu Wang, et al. ∙

research

∙ 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...

16 Pengcheng Li, et al. ∙

research

∙ 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...

0 Yongshun Gong, et al. ∙

research

∙ 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...

20 Xinlei Pan, et al. ∙

research

∙ 11/27/2018

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks

Depending on how much information an adversary can access to, adversaria...

6 Jinghui Chen, et al. ∙

research

∙ 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...

8 Lingfei Wu, et al. ∙

research

∙ 09/13/2018

Query-Efficient Black-Box Attack by Active Learning

Deep neural network (DNN) as a popular machine learning model is found t...

0 Pengcheng Li, et al. ∙

research

∙ 09/09/2018

Towards Query Efficient Black-box Attacks: An Input-free Perspective

Recent studies have highlighted that deep neural networks (DNNs) are vul...

0 Yali Du, et al. ∙

research

∙ 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...

0 Zaiyi Chen, et al. ∙

research

∙ 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 ...

12 Dong Su, et al. ∙

research

∙ 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...

2 Adnan Siraj Rakin, et al. ∙

research

∙ 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...

8 Minhao Cheng, et al. ∙

research

∙ 06/27/2018

Matrix Completion from Non-Uniformly Sampled Entries

In this paper, we consider matrix completion from non-uniformly sampled ...

4 Yuanyu Wan, et al. ∙

research

∙ 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...

0 Zhao Kang, et al. ∙

research

∙ 06/14/2018

Financial Forecasting and Analysis for Low-Wage Workers

Despite the plethora of financial services and products on the market no...

0 Wenyu Zhang, et al. ∙

research

∙ 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 ...

6 Chun-Chen Tu, et al. ∙

research

∙ 05/19/2018

Diverse Few-Shot Text Classification with Multiple Metrics

We study few-shot learning in natural language domains. Compared to many...

0 Mo Yu, et al. ∙

research

∙ 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...

0 Minhao Cheng, et al. ∙

research

∙ 02/14/2018

Edge Attention-based Multi-Relational Graph Convolutional Networks

Graph convolutional network (GCN) is generalization of convolutional neu...

0 Chao Shang, et al. ∙

research

∙ 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...

0 Mengying Sun, et al. ∙

research

∙ 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...

0 Tsui-Wei Weng, et al. ∙

research

∙ 12/06/2017

Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning

Modern neural image captioning systems typically adopt the encoder-decod...

0 Hongge Chen, et al. ∙

research

∙ 09/13/2017

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Recent studies have highlighted the vulnerability of deep neural network...

0 Pin-Yu Chen, et al. ∙

Jinfeng Yi

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