Preprint

Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective , Lu Wang, Xuanqing Liu, Jinfeng Yi, Zhi-Hua Zhou, Cho-Jui Hsieh. 2019.

An inexact subsampled proximal Newton-type method for large-scale machine learning , Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee, Yuekai Sun. 2017.

GPU-acceleration for Large-scale Tree Boosting , Huan Zhang, Si Si, Cho-Jui Hsieh. 2017.



Publications

A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks Huan Zhang*, Shiqi Wang*, Kaidi Xu*, Yihan Wang, Suman Jana, Cho-Jui Hsieh, Zico Kolter. (* Equal contributions) In ICML, 2022.

Extreme Zero-Shot Learning for Extreme Text Classification Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S Dhillon. In NAACL, 2022.

Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification. Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh and Hsiang-Fu Yu. In SIGIR, 2022.

CAT: Customized Adversarial Training for Improved Robustness Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit Dhillon, Cho-Jui Hsieh. In IJCAI, 2022.

Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks Jun-Ho Choi, Huan Zhang, Kim Jun-Hyuk, Cho jui Hsieh, Jong-Seok Lee. In ICPR, 2022.

Towards Efficient and Scalable Sharpness-Aware Minimization. Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You. In CVPR, 2022.

On the Sensitivity and Stability of Model Interpretations , Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang. In ACL, 2022.

Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples , Jianhan Xu*, Cenyuan Zhang*, Xiaoqing Zheng, Linyang Li, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang. In ACL-Findings, 2022.

Improving the Adversarial Robustness of NLP Models by Information Bottleneck , Cenyuan Zhang*, Xiang Zhou*, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh. In ACL-Findings, 2022.

Learning to Schedule Learning rate with Graph Neural Networks , Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh. To appear in ICLR, 2022.

Generalizing Few-Shot NAS with Gradient Matching. , Shoukang Hu*, Ruochen Wang*, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng. (* Equal contributions) To appear in ICLR, 2022.

On the Convergence of Certified Robust Training with Interval Bound Propagation , Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh. To appear in ICLR, 2022.

Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction , Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S Dhillon. To appear in ICLR, 2022.

Concurrent Adversarial Learning for Large-Batch Training , Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You. To appear in ICLR, 2022.

Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks , Qin Ding, Cho-Jui Hsieh, James Sharpnack. To appear in AISTATS, 2022.

A Review of Adversarial Attack and Defense for Classification Methods , Yao Li, Minhao Cheng, Cho-Jui Hsieh, Thomas Lee. The American Statistician, 2021.

DRONE: Data-aware Low-rank Compression for Large NLP Models , Patrick Chen, Hsiang-Fu Yu, Inderjit S Dhillon, Cho-Jui Hsieh. In NeurIPS, 2021.

Label Disentanglement in Partition-based Extreme Multilabel Classification. , Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S Dhillon. In NeurIPS, 2021.

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification , Shiqi Wang*, Huan Zhang*, Kaidi Xu*, Xue Lin, Suman Jana, Cho-Jui Hsieh, J Zico Kolter (* Equal Contribution). In NeurIPS, 2021.

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification. , Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh. In NeurIPS, 2021.

Fast Certified Robust Training with Short Warmup. , Zhouxing Shi*, Yihan Wang*, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh (* Equal Contribution). In NeurIPS, 2021.

Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding , Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio. In NeurIPS, 2021.

On the Transferability of Adversarial Attacksagainst Neural Text Classifier , Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, and Kai-Wei Chang. in EMNLP, 2021.

Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution. , Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, and Cho-Jui Hsieh. in EMNLP, 2021.

RANK-NOSH: Efficient Predictor-Based NAS via Non-Uniform Successive Halving , Ruochen Wang, Xiangning Chen, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh. in ICCV, 2021.

Towards Robustness of Deep Neural Networks via Regularization , Yao Li, Martin Renqiang Min, Thomas Lee, Wenchao Yu, Erik Kruus, Wei Wang, Cho-Jui Hsieh. In ICCV, 2021.

RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection , Yongming Rao, Benlin Liu, Yi Wei, Jiwen Lu, Cho-Jui Hsieh, Jie Zhou. In ICCV, 2021.

Overcoming Catastrophic Forgetting by Generative Regularization , Patrick H. Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai. In ICML, 2021.

Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble , Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, and Xuanjing Huang. In ACL, 2021.

Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging , Sara Imboden*, Xuanqing Liu*, Brandon S. Lee, Marie C. Payne, Cho-Jui Hsieh, Neil Y. C Lin (* Equal Contribution). Scientific Reports, 2021.

Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation , Chong Zhang, Jieyu Zhao, Huan Zhang, Kai-Wei Chang, and Cho-Jui Hsieh. In NAACL 2021.

Robust and Accurate Object Detection via Adversarial Learning. , Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong. In CVPR 2021.

Rethinking Architecture Selection in Differentiable NAS , Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh. To appear in ICLR 2021.

DrNAS: Dirichlet Neural Architecture Search , Xiangning Chen*, Ruochen Wang*, Minhao Cheng*, Xiaocheng Tang, Cho-Jui Hsieh (* Equal Contribution). To appear in ICLR 2021.

Robust Reinforcement Learning on State Observations with Learned Optimal Adversary , Huan Zhang*, Hongge Chen*, Duane Boning, Cho-Jui Hsieh (* Equal Contribution). To appear in ICLR 2021.

Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers , Kaidi Xu*, Huan Zhang*, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, Cho-Jui Hsieh (* Equal Contribution). To appear in ICLR 2021.

Evaluations and Methods for Explanation through Robustness Analysis , Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh. To appear in ICLR 2021.

Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search , Li-Cheng Lan, Meng-Yu Tsai, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh To appear in AAAI 2021.

Self-Progressing Robust Training , Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das In AAAI 2021.

Multi-Proxy Wasserstein Classifier for Image Classification. , Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh. In AAAI 2021.

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations , Huan Zhang*, Hongge Chen*, Chaowei Xiao, Bo Li, Mingyan Liu, Duane Boning, Cho-Jui Hsieh (* Equal Contribution). In NeurIPS 2020.

Multi-Stage Influence Function , Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, Cho-Jui Hsieh. In NeurIPS 2020.

Provably Robust Metric Learning , Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh. In NeurIPS 2020.

An Efficient Adversarial Attack for Tree Ensembles , Chong Zhang, Huan Zhang, Cho-Jui Hsieh. In NeurIPS 2020.

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond. , Kaidi Xu*, Zhouxing Shi*, Huan Zhang*, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh. (* Equal Contribution) In NeurIPS 2020.

Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data , Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee. In NeurIPS 2020.

Adversarially Robust Deep Image Super-Resolution using Entropy Regularization , Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee. In ACCV, 2020.

Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data , Lu Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Yuan Jiang. Machine Learning, 2020.

SSE-PT: Sequential Recommendation Via Personalized Transformer. , Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack. In the ACM Recommender Systems conference (RecSys), 2020.

Improved Adversarial Training via Learned Optimizer , Yuanhao Xiong, Cho-Jui Hsieh. In European Conference on Computer Vision (ECCV), 2020.

MetaDistiller: Network Self-boosting via Meta-learned Top-down Distillation , Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh. In European Conference on Computer Vision (ECCV), 2020.

Learning to Encode Position for Transformer with Continuous Dynamical Model , Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, Cho-Jui Hsieh. In International Conference on Machine Learning (ICML), 2020.

Stabilizing Differentiable Architecture Search via Perturbation-based Regularization , Xiangning Chen, Cho-Jui Hsieh. In International Conference on Machine Learning (ICML), 2020.

On Lp-norm Robustness of Ensemble Decision Stumps and Trees , Yihan Wang, Huan Zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh. In International Conference on Machine Learning (ICML), 2020.

Evaluating and enhancing the robustness of neural network-based dependency parsing models with adversarial examples , Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, Cho-Jui Hsieh, Minhao Cheng, Xuanjing Huang. In ACL (long), 2020.

What Does BERT with Vision Look At? , Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang. In ACL (short), 2020.

How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework , Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data , Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan. Journal of Machine Learning Research (JMLR), 2020.

Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems , Jyun-Yu Jiang*, Patrick H. Chen*, Cho-Jui Hsieh and Wei Wang. (* Equal Contribution) In Proceedings of the World Wide Web Conference (WWW), 2020.

Efficient Neural Interaction Functions Search for Collaborative Filtering , Quanming Yao*, Xiangning Chen*, James T. Kwok, Yong Li, Cho-Jui Hsieh (* Equal Contribution). In Proceedings of the World Wide Web Conference (WWW), 2020.

Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering , Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

Sign-OPT: A Query-Efficient Hard-label Adversarial Attack , Minhao Cheng*, Simranjit Singh*, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh (* Equal Contribution). In International Conference on Learning Representations (ICLR), 2020.


Robustness Verification for Transformers , Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh. In International Conference on Learning Representations (ICLR), 2020.


Learning to Learn by Zeroth-Order Oracle , Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh. In International Conference on Learning Representations (ICLR), 2020.


Towards Stable and Efficient Training of Verifiably Robust Neural Networks , Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane Boning, Cho-Jui Hsieh. In International Conference on Learning Representations (ICLR), 2020.


MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius , Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang. In International Conference on Learning Representations (ICLR), 2020.


Large Batch Optimization for Deep Learning: Training BERT in 76 minutes , Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh. In International Conference on Learning Representations (ICLR), 2020.


Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples , Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, Cho-Jui Hsieh. In AAAI Conference on Artificial Intelligence (AAAI), 2020.


ML-LOO: Detecting Adversarial Examples with Feature Attribution , Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan. In AAAI Conference on Artificial Intelligence (AAAI), 2020.


Convergence of Adversarial Training in Overparameterized Networks , Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho-Jui Hsieh, Jason D. Lee. To appear in NeurIPS 2019.


Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers , Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack. To appear in NeurIPS 2019.


Robustness Verification of Tree-based Models , Hongge Chen*, Huan Zhang*, Si Si, Yang Li, Duane Boing, Cho-Jui Hsieh. (* Equal contribution) To appear in NeurIPS, 2019.


A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, and Cho-Jui Hsieh. To appear in NeurIPS 2019.


A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks , Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, Pengchuan Zhang. To appear in NeurIPS 2019.


MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model , Yukun Ma*, Patrick H. Chen* and Cho-Jui Hsieh (* Equal contribution). To appear in EMNLP 2019.


Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks , Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee. To appear in ICCV 2019.


Fast LSTM Inference by Dynamic Decomposition on Cloud Systems , Y. You, Y. He, S. Rajbhandari, W. Wang, C.-J. Hsieh, K. Keutzer, J. Demmel. To appear in ICDM 2019.


Efficient Contextual Representation Learning Without Softmax Layer , Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, Kai-Wei Chang. To appear in TACL, 2019.


Large-batch Training for LSTM and Beyond , Yang You, Jonathan Hseu, Chris Ying, James Demmel, Kurt Keutzer, Cho-Jui Hsieh. The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2019.


Fast Deep Neural Network Training on Distributed Systems and Cloud TPUs , Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel, Kurt Keutzer. IEEE Transactions on Parallel and Distributed Systems, 2019.


On the Robustness of Self-Attentive Models , Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, Cho-Jui Hsieh. To appear In Proceedings of Association for Computational Linguistics (ACL), 2019.


Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks , Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh. To appear in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.


Robust Decision Trees Against Adversarial Examples , Hongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsieh. In International Conference on Machine Learning (ICML), 2019.


Rob-GAN: Generator, Discriminator, and Adversarial Attacker , Xuanqing Liu, Cho-Jui Hsieh. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2019.


Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent , Minhao Cheng, Wei Wei, Cho-Jui Hsieh. Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2019.


Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks , Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh. International Conference on Learning Representations (ICLR), 2019.


Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach , Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh. International Conference on Learning Representations (ICLR), 2019.


The Limitations of Adversarial Training and the Blind-Spot Attack , Huan Zhang*, Hongge Chen*, Zhao Song, Duane Boning, inderjit dhillon, Cho-Jui Hsieh. International Conference on Learning Representations (ICLR), 2019.


Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network , Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh. International Conference on Learning Representations (ICLR), 2019.


Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization , Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael Friedlander. SIAM International Conference on Data Mining (SDM), 2019.


A Fast Sampling Algorithm for Maximum Inner Product Search , Qin Ding, Hsiang-Fu Yu, Cho-Jui Hsieh. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.


Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables , Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit Dhillon. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.


RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications , Huan Zhang, Pengchuan Zhang, Cho-Jui Hsieh. In AAAI Conference on Artificial Intelligence (AAAI), 2019.


AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks , Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Shin-Ming Cheng. In AAAI Conference on Artificial Intelligence (AAAI), 2019.


GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking , Patrick Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh. In Advances in Neural Information Processing Systems (NIPS), 2018


Efficient Neural Network Robustness Certification with General Activation Functions , Huan Zhang*, Lily Weng*, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel. (* Equal contribution). In Advances in Neural Information Processing Systems (NIPS), 2018


Learning from Group Comparisons: Exploiting Higher Order Interactions , Yao Li, Minhao Cheng, Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh. In Advances in Neural Information Processing Systems (NIPS), 2018


Towards Robust Neural Networks via Random Self-ensemble , Xuanqing Liu, Minhao Cheng, Huan Zhang, Cho-Jui Hsieh. In European Conference on Computer Vision (ECCV), 2018.


ImageNet Training in Minutes , Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel, Kurt Keutzer. In International Conference on Parallel Processing (ICPP), 2018.


Fast Variance Reduction Method with Stochastic Batch Size , Xuanqing Liu, Cho-Jui Hsieh. In International Conference on Machine Learning (ICML), 2018.


Extreme Learning to Rank via Low Rank Assumption , Minhao Cheng, Cho-Jui Hsieh, Ian Davidson. In International Conference on Machine Learning (ICML), 2018.


SQL-Rank: A Listwise Approach to Collaborative Ranking , Liwei Wu, Cho-Jui Hsieh, James Sharpnack. In International Conference on Machine Learning (ICML), 2018.


Towards Fast Computation of Certified Robustness for ReLU Networks , Tsui-Wei Weng*, Huan Zhang*, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane Boning, Inderjit Dhillon, Luca Daniel. (* Equal contribution) In International Conference on Machine Learning (ICML), 2018.


Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems , Yang You, James Demmel, Cho-Jui Hsieh, Richard Vuduc. In International Conference on Supercomputing (ICS), 2018.


Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning , Hongge Chen*, Huan Zhang*, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh. (* Equal contribution) In Proceedings of Association for Computational Linguistics (ACL), 2018.


Distributed Primal-Dual Optimization for Non-uniformly Distributed Data. , Minhao Cheng, Cho-Jui Hsieh. In International Joint Conference on Artificial Intelligence (IJCAI), 2018


Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations , Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon. Journal of Machine Learning Research (JMLR), 2018.


Learning Word Embeddings for Low-resource Languages by PU Learning , Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang. Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2018.


Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach , Tsui-Wei Weng*, Huan Zhang*, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel (* Equal contribution). International Conference on Learning Representations (ICLR), 2018.


NLRR++: Scalable Subspace Clustering via Non-Convex Block Coordinate Descent , Jun Wang, Daming Shi, Cho-Jui Hsieh. SIAM International Conference on Data Mining (SDM), 2018.


EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples , Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh In AAAI Conference on Artificial Intelligence (AAAI), 2018.


ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models , Pin-Yu Chen*, Huan Zhang*, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh (* Equal contribution). ACM Conference on Computer and Communications Security (CCS) Workshop on Artificial Intelligence and Security (AISec), 2017.


Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent , Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu. In Advances in Neural Information Processing Systems (NIPS), 2017


Scalable Demand-Aware Recommendation , Jinfeng Yi, Cho-Jui Hsieh, Kush Varshney, Lijun Zhang, Yao Li. In Advances in Neural Information Processing Systems (NIPS), 2017


A Greedy Approach for Budgeted Maximum Inner Product Search , Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2017


A Hyperplane-based Algorithm for Semi-supervised Dimension Reduction , Huang Fang, Minhao Cheng, Cho-Jui Hsieh. In IEEE International Conference on Data Mining (ICDM), 2017.


Large-scale Collaborative Ranking in Near-Linear Time , Liwei Wu, Cho-Jui Hsieh, James Sharpnack. To appear in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.


Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines , Cho-Jui Hsieh, Si Si, Inderjit Dhillon. To appear in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.


Gradient Boosted Decision Trees for High Dimensional Sparse Output , Si Si, Huan Zhang, Sathiya Keerthi, Dhruv Mahajan, Inderjit Dhillon, Cho-Jui Hsieh. To appear in International Conference on Machine Learning (ICML) 34, 2017.


Improved Bounded Matrix Completion for Large-scale Recommender Systems , Huang Fang, Zhang Zhen, Yiqun Shao, Cho-Jui Hsieh. To appear in International Joint Conference on Artificial Intelligence (IJCAI), 2017.


Rank Aggregation and Prediction with Item Features , Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.


Memory Efficient Kernel Approximation , Si Si, Cho-Jui Hsieh, Inderjit Dhillon. Journal of Machine Learning Research (JMLR), 2017.


Machine Learning Meliorates Computing and Robustness in Discrete Combinatorial Optimization Problems , Fushing Hsieh, Kevin Fuji, Cho-Jui Hsieh. Frontiers in Applied Mathematics and Statistics, 2016.


Fixing the Convergence Problems in Parallel Asynchronous Dual Coordinate Descent , Huan Zhang, Cho-Jui Hsieh. In IEEE International Conference on Data Mining (ICDM), 2016.


HogWild++: A New Mechanism for Decentralized Asynchronous Stochastic Gradient Descent , Huan Zhang, Cho-Jui Hsieh, Venkatesh Akella. In IEEE International Conference on Data Mining (ICDM), 2016.


Asynchronous Parallel Greedy Coordinate Descent , Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit Dhillon, James Demmel, Cho-Jui Hsieh. In Advances in Neural Information Processing Systems (NIPS), 2016.


A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order , Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu, In Advances in Neural Information Processing Systems (NIPS), 2016.


Goal-Directed Inductive Matrix Completion, Si Si, Kai-Yang Chiang, Cho-Jui Hsieh, Nikhil Rao, Inderjit S. Dhillon. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.


Nomadic Computing for Big Data Analytics, Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S.V.N. Vishwanathan, Inderjit S. Dhillon. To appear at IEEE Computer, 2016.


Computationally Efficient Nystrom Approximation using Fast Transforms, Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML) 33, 2016.


Robust Principal Component Analysis with Side Information, Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML) 33, 2016.


Matrix Completion with Noisy Side Information, Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2015.


Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent, Ian E.H. Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2015.


PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML) 32, 2015.


PU Learning for Matrix Completion, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML) 32, 2015.


A Scalable Asynchronous Distributed Algorithm for Topic Modeling, Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S.V.N Vishwanathan, Inderjit S. Dhillon. In ACM WWW International conference on World Wide Web (WWW), 2015.


Fast Prediction for Large-Scale Kernel Machines, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2014.


QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models, Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Stephen Becker, Peder A. Olsen. In Advances in Neural Information Processing Systems (NIPS), 2014.


Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings, Ian E.H. Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2014.


NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion, Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S.V.N Vishwanathan, Inderjit S. Dhillon. In Prceedings of Very Large Databases (VLDB), 2014.


QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation, Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar. Journal of Machine Learning Research (JMLR), 2014.


Prediction and Clustering in Signed Networks: A Local to Global Perspective, Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Ambuj Tewari, and Inderjit S. Dhillon. Journal of Machine Learning Research (JMLR), 2014.


A Divide-and-Conquer Solver for Kernel Support Vector Machines, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML), 2014.


Memory Efficient Kernel Approximation, Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML), 2014. Recommended for JMLR Fast Track (18 out of 1260+).


Nuclear Norm Minimization via Active Subspace Selection, Cho-Jui Hsieh, Peder A. Olsen. In International Conference on Machine Learning (ICML), 2014.


BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables, Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Russell A. Poldrack. In Advances in Neural Information Processing Systems (NIPS), 2013. Oral presentation, 1.4% acceptance rate.


Large Scale Distributed Sparse Precision Estimation, Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2013.


Parallel matrix factorization for recommender systems, Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon, Knowledge and Information Systems (KAIS), Sept, 2013.


Organizational Overlap on Social Networks and its Applications, Cho-Jui Hsieh, Mitul Tiwari, Deepack Agarwal, Xinyi Huang, Sam Shah. In ACM WWW International conference on World Wide Web (WWW), 2013.


Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems, Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon. In IEEE International Conference on Data Mining(ICDM), 2012. ICDM best paper award


A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation, Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Arindam Banerjee. In Advances in Neural Information Processing Systems (NIPS) 25, 2012.


Low-Rank Modeling of Signed Networks, Cho-Jui Hsieh, Kai-Yang Chiang, Inderjit S. Dhillon. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2012.


Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar. In Advances in Neural Information Processing Systems (NIPS) 24, 2011.


Fast Coordinate Descent Methods with Variable Selection for Non-negative Matrix Factorization, Cho-Jui Hsieh, Inderjit S. Dhillon, In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011).


Large linear classification when data cannot fit in memory, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin. In ACM SIGKDD International Conference on Kowledge Discovery and Data Mining (KDD 2010). KDD best research paper award.

A short version appears in IJCAI 2011. A journal version appears in ACM Transactions on Knowledge Discovery from Data (TKDD), Volumn 5 Issue 4 (2012).


A Comparison of Optimization methods and software for large-scale L1-regularized linear classification, Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 11(2010), 3183-3234.


Training and Testing Low-degree Polynomial Data Mappings via Linear SVM, Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 11(2010), 1471--1490.


An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes, H.-Y. Lo, K.-W. Chang, S.-T. Chen, T.-H. Chiang, C.-S. Ferng, C.-J. Hsieh, Y.-K. Ko, T.-T. Kuo, H.-C. Lai, K.-Y. Lin, C.-H. Wang, H.-F. Yu, C.-J. Lin, H.-T. Lin and S.-d. Lin, JMLR Workshop and Conference Proceedings, V.7, 57-64, 2009 (Third Place of the KDDCup'09 Slow Track).


Iterative scaling and coordinate descent methods for maximum entropy models, Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, and Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 11(2010), 581-614.


LIBLINEAR: A library for large linear classification, Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 9(2008), 1871-1874.


A sequential dual method for large scale multi-class linear SVMs, S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2008.


A dual coordinate descent method for large-scale linear SVM, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, and S. Sundararajan. In International Conference on Machine Learning (ICML) 25, 2008.


Coordinate descent method for large-scale L2-loss linear SVM, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 9(2008), 1369-1398.