
Fast Certified Robust Training via Better Initialization and Shorter Warmup
Recently, bound propagation based certified adversarial defense have bee...
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On the Adversarial Robustness of Visual Transformers
Following the success in advancing natural language processing and under...
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Robust and Accurate Object Detection via Adversarial Learning
Data augmentation has become a de facto component for training highperf...
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BetaCROWN: Efficient Bound Propagation with Perneuron Split Constraints for Complete and Incomplete Neural Network Verification
Recent works in neural network verification show that cheap incomplete v...
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Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
We study the robustness of reinforcement learning (RL) with adversariall...
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Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks
Convolutional neural networks (CNNs) are widely used to recognize the us...
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Robust Text CAPTCHAs Using Adversarial Examples
CAPTCHA (Completely Automated Public Truing test to tell Computers and H...
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SelfProgressing Robust Training
Enhancing model robustness under new and even adversarial environments i...
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Learning to Stop: Dynamic Simulation MonteCarlo Tree Search
Monte Carlo tree search (MCTS) has achieved stateoftheart results in ...
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Voting based ensemble improves robustness of defensive models
Developing robust models against adversarial perturbations has been an a...
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Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Formal verification of neural networks (NNs) is a challenging and import...
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Generating universal language adversarial examples by understanding and enhancing the transferability across neural models
Deep neural network models are vulnerable to adversarial attacks. In man...
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An Efficient Adversarial Attack for Tree Ensembles
We study the problem of efficient adversarial attacks on tree based ense...
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How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
Many optimizers have been proposed for training deep neural networks, an...
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MetaDistiller: Network SelfBoosting via MetaLearned TopDown Distillation
Knowledge Distillation (KD) has been one of the most popular methods to...
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On ℓ_pnorm Robustness of Ensemble Stumps and Trees
Recent papers have demonstrated that ensemble stumps and trees could be ...
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Improving the Speed and Quality of GAN by Adversarial Training
Generative adversarial networks (GAN) have shown remarkable results in i...
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Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
Despite neural networks have achieved prominent performance on many natu...
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DrNAS: Dirichlet Neural Architecture Search
This paper proposes a novel differentiable architecture search method by...
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The Limit of the Batch Size
Largebatch training is an efficient approach for current distributed de...
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Provably Robust Metric Learning
Metric learning is an important family of algorithms for classification ...
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An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
We consider the contextual bandit problem, where a player sequentially m...
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Evaluations and Methods for Explanation through Robustness Analysis
Among multiple ways of interpreting a machine learning model, measuring ...
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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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Improved Adversarial Training via Learned Optimizer
Adversarial attack has recently become a tremendous threat to deep learn...
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Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations
Deep Reinforcement Learning (DRL) is vulnerable to small adversarial per...
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Learning to Encode Position for Transformer with Continuous Dynamical Model
We introduce a new way of learning to encode position information for no...
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Automatic Perturbation Analysis on General Computational Graphs
Linear relaxation based perturbation analysis for neural networks, which...
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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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Robustness Verification for Transformers
Robustness verification that aims to formally certify the prediction beh...
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Multiscale Nonstationary Stochastic Bandits
Classic contextual bandit algorithms for linear models, such as LinUCB, ...
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Stabilizing Differentiable Architecture Search via Perturbationbased Regularization
Differentiable architecture search (DARTS) is a prevailing NAS solution ...
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MACER: Attackfree and Scalable Robust Training via Maximizing Certified Radius
Adversarial training is one of the most popular ways to learn robust mod...
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GraphDefense: Towards Robust Graph Convolutional Networks
In this paper, we study the robustness of graph convolutional networks (...
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Enhancing Certifiable Robustness via a Deep Model Ensemble
We propose an algorithm to enhance certified robustness of a deep model ...
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A Unified Framework for Data Poisoning Attack to Graphbased Semisupervised Learning
In this paper, we proposed a general framework for data poisoning attack...
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Learning to Learn by ZerothOrder Oracle
In the learning to learn (L2L) framework, we cast the design of optimiza...
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ElasticInfoGAN: Unsupervised Disentangled Representation Learning in Imbalanced Data
We propose a novel unsupervised generative model, ElasticInfoGAN, that ...
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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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Toward Finding The Global Optimal of Adversarial Examples
Current machine learning models are vulnerable to adversarial examples (...
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Natural Adversarial Sentence Generation with Gradientbased Perturbation
This work proposes a novel algorithm to generate natural language advers...
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Temporal Collaborative Ranking Via Personalized Transformer
The collaborative ranking problem has been an important open research qu...
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VisualBERT: A Simple and Performant Baseline for Vision and Language
We propose VisualBERT, a simple and flexible framework for modeling a br...
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Convergence of Adversarial Training in Overparametrized Networks
Neural networks are vulnerable to adversarial examples, i.e. inputs that...
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Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Training neural networks with verifiable robustness guarantees is challe...
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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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Robustness Verification of Treebased Models
We study the robustness verification problem for treebased models, incl...
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MLLOO: Detecting Adversarial Examples with Feature Attribution
Deep neural networks obtain stateoftheart performance on a series of ...
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Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
Neural Ordinary Differential Equation (Neural ODE) has been proposed as ...
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Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
In this paper, we consider recommender systems with side information in ...
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ChoJui Hsieh
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Assistant professor of Computer Science and Statistics at UC Davis