
Provably Robust Metric Learning
Metric learning is an important family of algorithms for classification ...
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Spanning Attack: Reinforce Blackbox Attacks with Unlabeled Data
Adversarial blackbox attacks aim to craft adversarial perturbations by ...
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Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
Recently, practical applications for passenger flow prediction have brou...
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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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Characterizing Attacks on Deep Reinforcement Learning
Deep reinforcement learning (DRL) has achieved great success in various ...
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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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Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and po...
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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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Evaluating the Robustness of Nearest Neighbor Classifiers: A PrimalDual Perspective
We study the problem of computing the minimum adversarial perturbation o...
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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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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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How You Act Tells a Lot: PrivacyLeakage Attack on Deep Reinforcement Learning
Machine learning has been widely applied to various applications, some o...
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A FrankWolfe Framework for Efficient and Effective Adversarial Attacks
Depending on how much information an adversary can access to, adversaria...
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Random Warping Series: A Random Features Method for TimeSeries Embedding
Time series data analytics has been a problem of substantial interests f...
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QueryEfficient BlackBox Attack by Active Learning
Deep neural network (DNN) as a popular machine learning model is found t...
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Towards Query Efficient Blackbox Attacks: An Inputfree Perspective
Recent studies have highlighted that deep neural networks (DNNs) are vul...
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Universal Stagewise Learning for NonConvex Problems with Convergence on Averaged Solutions
Although stochastic gradient descent () method and its variants (e.g., s...
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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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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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QueryEfficient Hardlabel Blackbox Attack:An Optimizationbased Approach
We study the problem of attacking a machine learning model in the hardl...
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Matrix Completion from NonUniformly Sampled Entries
In this paper, we consider matrix completion from nonuniformly sampled ...
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Selfweighted Multiple Kernel Learning for Graphbased Clustering and Semisupervised Classification
Multiple kernel learning (MKL) method is generally believed to perform b...
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Financial Forecasting and Analysis for LowWage Workers
Despite the plethora of financial services and products on the market no...
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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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Diverse FewShot Text Classification with Multiple Metrics
We study fewshot learning in natural language domains. Compared to many...
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Seq2Sick: Evaluating the Robustness of SequencetoSequence Models with Adversarial Examples
Crafting adversarial examples has become an important technique to evalu...
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Edge Attentionbased MultiRelational Graph Convolutional Networks
Graph convolutional network (GCN) is generalization of convolutional neu...
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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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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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ShowandFool: Crafting Adversarial Examples for Neural Image Captioning
Modern neural image captioning systems typically adopt the encoderdecod...
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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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Robust Task Clustering for Deep ManyTask Learning
We investigate task clustering for deeplearning based multitask and fe...
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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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Similarity Preserving Representation Learning for Time Series Analysis
A considerable amount of machine learning algorithms take instancefeatu...
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Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
This work focuses on dynamic regret of online convex optimization that c...
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Jinfeng Yi
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Research Staff Member at IBM Thomas J. Watson Research Center since 2015, Postdoctoral Researcher at IBM Thomas J. Watson Research Center 20142015, Research Intern at IBM Thomas J. Watson Research Center 2013, Consultant at Stroz Friedberg, LLC 2012