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2020 – today
- 2021
[j21]Yang You
, Jingyue Huang, Cho-Jui Hsieh, Richard W. Vuduc, James Demmel:
Communication-avoiding kernel ridge regression on parallel and distributed systems. CCF Trans. High Perform. Comput. 3(3): 252-270 (2021)- [c130]Li-Cheng Lan, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh:
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search. AAAI 2021: 259-267 - [c129]Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das:
Self-Progressing Robust Training. AAAI 2021: 7107-7115 - [c128]Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh:
Multi-Proxy Wasserstein Classifier for Image Classification. AAAI 2021: 8618-8626 - [c127]Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang:
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble. ACL/IJCNLP (1) 2021: 5482-5492 - [c126]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. AISTATS 2021: 1585-1593 - [c125]Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong:
Robust and Accurate Object Detection via Adversarial Learning. CVPR 2021: 16622-16631 - [c124]Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang:
On the Transferability of Adversarial Attacks against Neural Text Classifier. EMNLP (1) 2021: 1612-1625 - [c123]Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh:
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution. EMNLP (1) 2021: 3137-3147 - [c122]Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh:
DrNAS: Dirichlet Neural Architecture Search. ICLR 2021 - [c121]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. ICLR 2021 - [c120]Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh:
Rethinking Architecture Selection in Differentiable NAS. ICLR 2021 - [c119]Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, Cho-Jui Hsieh:
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers. ICLR 2021 - [c118]Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh:
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary. ICLR 2021 - [c117]Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai:
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization. ICML 2021: 1760-1770 - [c116]Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu:
Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). KDD 2021: 4112-4113 - [c115]Sunipa Dev, Mehrnoosh Sameki, Jwala Dhamala, Cho-Jui Hsieh:
Measures and Best Practices for Responsible AI. KDD 2021: 4118 - [c114]Chong Zhang, Jieyu Zhao, Huan Zhang, Kai-Wei Chang, Cho-Jui Hsieh:
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation. NAACL-HLT 2021: 3899-3916 - [i126]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
Robust Text CAPTCHAs Using Adversarial Examples. CoRR abs/2101.02483 (2021) - [i125]Seong-Eun Moon, Chun-Jui Chen, Cho-Jui Hsieh, Jane-Ling Wang, Jong-Seok Lee:
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks. CoRR abs/2101.07069 (2021) - [i124]Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh:
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary. CoRR abs/2101.08452 (2021) - [i123]Hojung Lee, Cho-Jui Hsieh, Jong-Seok Lee:
Local Critic Training for Model-Parallel Learning of Deep Neural Networks. CoRR abs/2102.01963 (2021) - [i122]Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification. CoRR abs/2103.06624 (2021) - [i121]Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong:
Robust and Accurate Object Detection via Adversarial Learning. CoRR abs/2103.13886 (2021) - [i120]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
On the Adversarial Robustness of Visual Transformers. CoRR abs/2103.15670 (2021) - [i119]Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
Fast Certified Robust Training via Better Initialization and Shorter Warmup. CoRR abs/2103.17268 (2021) - [i118]Chong Zhang, Jieyu Zhao, Huan Zhang, Kai-Wei Chang, Cho-Jui Hsieh:
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation. CoRR abs/2104.05232 (2021) - [i117]Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang:
On the Faithfulness Measurements for Model Interpretations. CoRR abs/2104.08782 (2021) - [i116]Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen, Sathish Thoppay, Cho-Jui Hsieh, Lior Shapira, Radu Soricut, Hartwig Adam, Matthew Brown, Ming-Hsuan Yang, Boqing Gong:
2.5D Visual Relationship Detection. CoRR abs/2104.12727 (2021) - [i115]Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee:
Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks. CoRR abs/2104.15022 (2021) - [i114]Yao Li, Tongyi Tang, Cho-Jui Hsieh, Thomas C. M. Lee:
Detecting Adversarial Examples with Bayesian Neural Network. CoRR abs/2105.08620 (2021) - [i113]Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam, Cho-Jui Hsieh, Kishore Papineni, Sanjiv Kumar:
Balancing Robustness and Sensitivity using Feature Contrastive Learning. CoRR abs/2105.09394 (2021) - [i112]Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You:
Concurrent Adversarial Learning for Large-Batch Training. CoRR abs/2106.00221 (2021) - [i111]Xiangning Chen, Cho-Jui Hsieh, Boqing Gong:
When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations. CoRR abs/2106.01548 (2021) - [i110]Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh:
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification. CoRR abs/2106.02034 (2021) - [i109]Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio:
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding. CoRR abs/2106.02795 (2021) - [i108]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. CoRR abs/2106.02978 (2021) - [i107]Qin Ding, Yi-Wei Liu, Cho-Jui Hsieh, James Sharpnack:
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. CoRR abs/2106.02979 (2021) - [i106]Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Label Disentanglement in Partition-based Extreme Multilabel Classification. CoRR abs/2106.12751 (2021) - [i105]Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh:
Rethinking Architecture Selection in Differentiable NAS. CoRR abs/2108.04392 (2021) - [i104]Yongming Rao, Benlin Liu, Yi Wei, Jiwen Lu, Cho-Jui Hsieh, Jie Zhou:
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection. CoRR abs/2108.07794 (2021) - [i103]Ruochen Wang, Xiangning Chen, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh:
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving. CoRR abs/2108.08019 (2021) - [i102]Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh:
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution. CoRR abs/2108.12777 (2021) - [i101]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Training Meta-Surrogate Model for Transferable Adversarial Attack. CoRR abs/2109.01983 (2021) - [i100]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Adversarial Attack across Datasets. CoRR abs/2110.07718 (2021) - [i99]Rulin Shao, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
How and When Adversarial Robustness Transfers in Knowledge Distillation? CoRR abs/2110.12072 (2021) - [i98]Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon:
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction. CoRR abs/2111.00064 (2021) - [i97]Shanda Li, Xiangning Chen, Di He, Cho-Jui Hsieh:
Can Vision Transformers Perform Convolution? CoRR abs/2111.01353 (2021) - [i96]Yao Li, Minhao Cheng, Cho-Jui Hsieh, Thomas C. M. Lee:
A Review of Adversarial Attack and Defense for Classification Methods. CoRR abs/2111.09961 (2021) - 2020
- [j20]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. J. Mach. Learn. Res. 21: 43:1-43:36 (2020) - [j19]Yang You, Yuxiong He, Samyam Rajbhandari, Wenhan Wang, Cho-Jui Hsieh, Kurt Keutzer, James Demmel:
Fast LSTM by dynamic decomposition on cloud and distributed systems. Knowl. Inf. Syst. 62(11): 4169-4197 (2020) - [j18]Lu Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Yuan Jiang:
Spanning attack: reinforce black-box attacks with unlabeled data. Mach. Learn. 109(12): 2349-2368 (2020) - [j17]Seong-Eun Moon, Chun-Jui Chen, Cho-Jui Hsieh, Jane-Ling Wang, Jong-Seok Lee:
Emotional EEG classification using connectivity features and convolutional neural networks. Neural Networks 132: 96-107 (2020) - [c113]Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, Cho-Jui Hsieh:
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples. AAAI 2020: 3601-3608 - [c112]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
ML-LOO: Detecting Adversarial Examples with Feature Attribution. AAAI 2020: 6639-6647 - [c111]Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee:
Adversarially Robust Deep Image Super-Resolution Using Entropy Regularization. ACCV (4) 2020: 301-317 - [c110]Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang:
What Does BERT with Vision Look At? ACL 2020: 5265-5275 - [c109]Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, Cho-Jui Hsieh, Minhao Cheng, Xuanjing Huang:
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples. ACL 2020: 6600-6610 - [c108]Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh:
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. AISTATS 2020: 776-787 - [c107]Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh:
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework. CVPR 2020: 279-287 - [c106]Yuanhao Xiong, Cho-Jui Hsieh:
Improved Adversarial Training via Learned Optimizer. ECCV (8) 2020: 85-100 - [c105]Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh:
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation. ECCV (14) 2020: 694-709 - [c104]Minhao Cheng, Simranjit Singh, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh:
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack. ICLR 2020 - [c103]Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh:
Learning to Learn by Zeroth-Order Oracle. ICLR 2020 - [c102]Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh:
Robustness Verification for Transformers. ICLR 2020 - [c101]Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh:
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. ICLR 2020 - [c100]Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang:
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. ICLR 2020 - [c99]Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane S. Boning, Cho-Jui Hsieh:
Towards Stable and Efficient Training of Verifiably Robust Neural Networks. ICLR 2020 - [c98]Xiangning Chen, Cho-Jui Hsieh:
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization. ICML 2020: 1554-1565 - [c97]Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
Learning to Encode Position for Transformer with Continuous Dynamical Model. ICML 2020: 6327-6335 - [c96]Yihan Wang, Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh:
On Lp-norm Robustness of Ensemble Decision Stumps and Trees. ICML 2020: 10104-10114 - [c95]Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane S. Boning, Cho-Jui Hsieh:
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations. NeurIPS 2020 - [c94]Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. NeurIPS 2020 - [c93]Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee:
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data. NeurIPS 2020 - [c92]Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh:
Provably Robust Metric Learning. NeurIPS 2020 - [c91]Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh:
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond. NeurIPS 2020 - [c90]Chong Zhang, Huan Zhang, Cho-Jui Hsieh:
An Efficient Adversarial Attack for Tree Ensembles. NeurIPS 2020 - [c89]Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack:
SSE-PT: Sequential Recommendation Via Personalized Transformer. RecSys 2020: 328-337 - [c88]Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li, Cho-Jui Hsieh:
Efficient Neural Interaction Function Search for Collaborative Filtering. WWW 2020: 1660-1670 - [c87]Jyun-Yu Jiang, Patrick H. Chen, Cho-Jui Hsieh, Wei Wang:
Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems. WWW 2020: 2177-2187 - [i95]Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang:
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. CoRR abs/2001.02378 (2020) - [i94]Xiangning Chen, Cho-Jui Hsieh:
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization. CoRR abs/2002.05283 (2020) - [i93]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Multiscale Non-stationary Stochastic Bandits. CoRR abs/2002.05289 (2020) - [i92]Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh:
Robustness Verification for Transformers. CoRR abs/2002.06622 (2020) - [i91]Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, Cho-Jui Hsieh:
CAT: Customized Adversarial Training for Improved Robustness. CoRR abs/2002.06789 (2020) - [i90]Kaidi Xu, Zhouxing Shi, Huan Zhang, Minlie Huang, Kai-Wei Chang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh:
Automatic Perturbation Analysis on General Computational Graphs. CoRR abs/2002.12920 (2020) - [i89]Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Duane S. Boning, Cho-Jui Hsieh:
Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations. CoRR abs/2003.08938 (2020) - [i88]Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
Learning to Encode Position for Transformer with Continuous Dynamical Model. CoRR abs/2003.09229 (2020) - [i87]Yuanhao Xiong, Cho-Jui Hsieh:
Improved Adversarial Training via Learned Optimizer. CoRR abs/2004.12227 (2020) - [i86]Lu Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Yuan Jiang:
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data. CoRR abs/2005.04871 (2020) - [i85]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. CoRR abs/2006.00442 (2020) - [i84]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. CoRR abs/2006.04012 (2020) - [i83]Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh:
Provably Robust Metric Learning. CoRR abs/2006.07024 (2020) - [i82]Yang You, Yuhui Wang, Huan Zhang, Zhao Zhang, James Demmel, Cho-Jui Hsieh:
The Limit of the Batch Size. CoRR abs/2006.08517 (2020) - [i81]Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh:
DrNAS: Dirichlet Neural Architecture Search. CoRR abs/2006.10355 (2020) - [i80]Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang:
Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble. CoRR abs/2006.11627 (2020) - [i79]Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. CoRR abs/2007.09081 (2020) - [i78]Jiachen Zhong, Xuanqing Liu, Cho-Jui Hsieh:
Improving the Speed and Quality of GAN by Adversarial Training. CoRR abs/2008.03364 (2020) - [i77]Yihan Wang, Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh:
On 𝓁p-norm Robustness of Ensemble Stumps and Trees. CoRR abs/2008.08755 (2020) - [i76]Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh:
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation. CoRR abs/2008.12094 (2020) - [i75]Yuanhao Xiong, Xuanqing Liu, Li-Cheng Lan, Yang You, Si Si, Cho-Jui Hsieh:
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers. CoRR abs/2010.09889 (2020) - [i74]Chong Zhang, Huan Zhang, Cho-Jui Hsieh:
An Efficient Adversarial Attack for Tree Ensembles. CoRR abs/2010.11598 (2020) - [i73]Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang:
Generating universal language adversarial examples by understanding and enhancing the transferability across neural models. CoRR abs/2011.08558 (2020) - [i72]Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, Cho-Jui Hsieh:
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers. CoRR abs/2011.13824 (2020) - [i71]Devvrit, Minhao Cheng, Cho-Jui Hsieh, Inderjit S. Dhillon:
Voting based ensemble improves robustness of defensive models. CoRR abs/2011.14031 (2020) - [i70]Li-Cheng Lan, Meng-Yu Tsai, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh:
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search. CoRR abs/2012.07910 (2020) - [i69]Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das:
Self-Progressing Robust Training. CoRR abs/2012.11769 (2020)
2010 – 2019
- 2019
- [j16]Jiarui Fang, Haohuan Fu, Guangwen Yang, Cho-Jui Hsieh:
RedSync: Reducing synchronization bandwidth for distributed deep learning training system. J. Parallel Distributed Comput. 133: 30-39 (2019) - [j15]Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, Kai-Wei Chang:
Efficient Contextual Representation Learning With Continuous Outputs. Trans. Assoc. Comput. Linguistics 7: 611-624 (2019) - [j14]Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel, Kurt Keutzer:
Fast Deep Neural Network Training on Distributed Systems and Cloud TPUs. IEEE Trans. Parallel Distributed Syst. 30(11): 2449-2462 (2019) - [c86]Chun-Chen Tu, Pai-Shun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Shin-Ming Cheng:
AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks. AAAI 2019: 742-749 - [c85]Huan Zhang, Pengchuan Zhang, Cho-Jui Hsieh:
RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications. AAAI 2019: 5757-5764 - [c84]Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, Cho-Jui Hsieh:
On the Robustness of Self-Attentive Models. ACL (1) 2019: 1520-1529 - [c83]Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables. AISTATS 2019: 2641-2649 - [c82]Qin Ding, Hsiang-Fu Yu, Cho-Jui Hsieh:
A Fast Sampling Algorithm for Maximum Inner Product Search. AISTATS 2019: 3004-3012 - [c81]Xuanqing Liu, Cho-Jui Hsieh:
Rob-GAN: Generator, Discriminator, and Adversarial Attacker. CVPR 2019: 11234-11243 - [c80]Yukun Ma, Patrick H. Chen, Cho-Jui Hsieh:
MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model. EMNLP/IJCNLP (1) 2019: 5256-5265 - [c79]Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, Huan Zhang, Cho-Jui Hsieh, Mani B. Srivastava:
GenAttack: practical black-box attacks with gradient-free optimization. GECCO 2019: 1111-1119 - [c78]Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee:
Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks. ICCV 2019: 303-311 - [c77]Yang You, Yuxiong He, Samyam Rajbhandari, Wenhan Wang, Cho-Jui Hsieh, Kurt Keutzer, James Demmel:
Fast LSTM Inference by Dynamic Decomposition on Cloud Systems. ICDM 2019: 748-757 - [c76]Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. ICLR (Poster) 2019 - [c75]Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach. ICLR (Poster) 2019 - [c74]Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh:
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network. ICLR (Poster) 2019 - [c73]Huan Zhang, Hongge Chen, Zhao Song, Duane S. Boning, Inderjit S. Dhillon, Cho-Jui Hsieh:
The Limitations of Adversarial Training and the Blind-Spot Attack. ICLR (Poster) 2019 - [c72]Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh:
Robust Decision Trees Against Adversarial Examples. ICML 2019: 1122-1131 - [c71]Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh:
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. KDD 2019: 257-266 - [c70]Minhao Cheng, Wei Wei, Cho-Jui Hsieh:
Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent. NAACL-HLT (1) 2019: 3325-3335 - [c69]Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L. Sharpnack:
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. NeurIPS 2019: 24-34 - [c68]Xuanqing Liu, Si Si, Jerry Zhu, Yang Li, Cho-Jui Hsieh:
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. NeurIPS 2019: 9777-9787 - [c67]Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, Pengchuan Zhang:
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks. NeurIPS 2019: 9832-9842 - [c66]Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane S. Boning, Cho-Jui Hsieh:
Robustness Verification of Tree-based Models. NeurIPS 2019: 12317-12328 - [c65]