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2020 – today
- 2024
[j34]Xiawei Wang
, Yao Li, Cho-Jui Hsieh, Thomas C. M. Lee:
Uncovering Distortion Differences: A Study of Adversarial Attacks and Machine Discriminability. IEEE Access 12: 117872-117883 (2024)- [j33]Jaehui Hwang, Huan Zhang, Jun-Ho Choi, Cho-Jui Hsieh, Jong-Seok Lee:
Temporal shuffling for defending deep action recognition models against adversarial attacks. Neural Networks 169: 388-397 (2024) - [j32]Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, Cho-Jui Hsieh:
Red Teaming Language Model Detectors with Language Models. Trans. Assoc. Comput. Linguistics 12: 174-189 (2024) - [j31]Yao Li, Tongyi Tang, Cho-Jui Hsieh, Thomas C. M. Lee:
Adversarial Examples Detection With Bayesian Neural Network. IEEE Trans. Emerg. Top. Comput. Intell. 8(5): 3654-3664 (2024) - [j30]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits. Trans. Mach. Learn. Res. 2024 (2024) - [j29]Tong Xie, Haoyu Li, Andrew Bai, Cho-Jui Hsieh:
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation. Trans. Mach. Learn. Res. 2024 (2024) - [c210]Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Wei Wang:
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering. ACL (1) 2024: 254-266 - [c209]Cho-Jui Hsieh, Si Si, Felix Yu, Inderjit S. Dhillon:
Automatic Engineering of Long Prompts. ACL (Findings) 2024: 10672-10685 - [c208]Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh:
Defending LLMs against Jailbreaking Attacks via Backtranslation. ACL (Findings) 2024: 16031-16046 - [c207]Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Boqing Gong, Cho-Jui Hsieh:
Understanding the Impact of Negative Prompts: When and How Do They Take Effect? ECCV (89) 2024: 190-206 - [c206]Yuanhao Xiong, Yixin Nie, Haotian Liu, Boxin Wang, Jun Chen, Rong Jin, Cho-Jui Hsieh, Lorenzo Torresani, Jie Lei:
UNICORN: A Unified Causal Video-Oriented Language-Modeling Framework for Temporal Video-Language Tasks. EMNLP 2024: 12983-12997 - [c205]Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, Cho-Jui Hsieh:
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLMs Jailbreakers. EMNLP (Findings) 2024: 13891-13913 - [c204]Kuei-Chun Kao, Ruochen Wang, Cho-Jui Hsieh:
Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns? EMNLP (Findings) 2024: 16821-16843 - [c203]Sai Surya Duvvuri, Devvrit, Rohan Anil, Cho-Jui Hsieh, Inderjit S. Dhillon:
Combining Axes Preconditioners through Kronecker Approximation for Deep Learning. ICLR 2024 - [c202]Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar:
Two-stage LLM Fine-tuning with Less Specialization and More Generalization. ICLR 2024 - [c201]Yuanhao Xiong, Long Zhao, Boqing Gong, Ming-Hsuan Yang, Florian Schroff, Ting Liu, Cho-Jui Hsieh, Liangzhe Yuan:
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding. ICLR 2024 - [c200]Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun, Chien Feng, Cho-Jui Hsieh, Chun-Yi Lee:
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning. ICML 2024 - [c199]Justin Cui, Ruochen Wang, Yuanhao Xiong, Cho-Jui Hsieh:
Ameliorate Spurious Correlations in Dataset Condensation. ICML 2024 - [c198]Ruochen Wang, Ting Liu, Cho-Jui Hsieh, Boqing Gong:
On Discrete Prompt Optimization for Diffusion Models. ICML 2024 - [c197]Ruochen Wang, Sohyun An, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, Cho-Jui Hsieh:
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts. ICML 2024 - [c196]Lujie Yang, Hongkai Dai, Zhouxing Shi, Cho-Jui Hsieh, Russ Tedrake, Huan Zhang:
Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation. ICML 2024 - [c195]Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar:
Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features. KDD 2024: 1360-1371 - [c194]Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan:
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models. WSDM 2024: 77-86 - [c193]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Entity Disambiguation with Extreme Multi-label Ranking. WWW 2024: 4172-4180 - [i194]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. CoRR abs/2401.07298 (2024) - [i193]Tong Xie, Haoyu Li, Andrew Bai, Cho-Jui Hsieh:
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation. CoRR abs/2401.09031 (2024) - [i192]Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun, Chien Feng, Cho-Jui Hsieh, Chun-Yi Lee:
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning. CoRR abs/2402.01057 (2024) - [i191]Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly:
Which Pretrain Samples to Rehearse when Finetuning Pretrained Models? CoRR abs/2402.08096 (2024) - [i190]Sen Li, Ruochen Wang, Cho-Jui Hsieh, Minhao Cheng, Tianyi Zhou:
MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion. CoRR abs/2402.12741 (2024) - [i189]Yong Liu, Zirui Zhu, Chaoyu Gong, Minhao Cheng, Cho-Jui Hsieh, Yang You:
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning. CoRR abs/2402.15751 (2024) - [i188]Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh:
Defending LLMs against Jailbreaking Attacks via Backtranslation. CoRR abs/2402.16459 (2024) - [i187]Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, Cho-Jui Hsieh:
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers. CoRR abs/2402.16914 (2024) - [i186]Lujie Yang, Hongkai Dai, Zhouxing Shi, Cho-Jui Hsieh, Russ Tedrake, Huan Zhang:
Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation for Efficient Synthesis and Verification. CoRR abs/2404.07956 (2024) - [i185]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Low-rank Matrix Bandits with Heavy-tailed Rewards. CoRR abs/2404.17709 (2024) - [i184]Siddhant Kharbanda, Devaansh Gupta, Gururaj K, Pankaj Malhotra, Cho-Jui Hsieh, Rohit Babbar:
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification. CoRR abs/2405.03714 (2024) - [i183]Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar:
Learning label-label correlations in Extreme Multi-label Classification via Label Features. CoRR abs/2405.04545 (2024) - [i182]Justin Cui, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh:
OR-Bench: An Over-Refusal Benchmark for Large Language Models. CoRR abs/2405.20947 (2024) - [i181]Zhouxing Shi, Qirui Jin, Zico Kolter, Suman Jana, Cho-Jui Hsieh, Huan Zhang:
Neural Network Verification with Branch-and-Bound for General Nonlinearities. CoRR abs/2405.21063 (2024) - [i180]Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Boqing Gong, Cho-Jui Hsieh, Minhao Cheng:
The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise. CoRR abs/2406.01970 (2024) - [i179]Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Boqing Gong, Cho-Jui Hsieh:
Understanding the Impact of Negative Prompts: When and How Do They Take Effect? CoRR abs/2406.02965 (2024) - [i178]Minzhou Pan, Yi Zeng, Xue Lin, Ning Yu, Cho-Jui Hsieh, Peter Henderson, Ruoxi Jia:
JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model Edits. CoRR abs/2406.03720 (2024) - [i177]Justin Cui, Ruochen Wang, Yuanhao Xiong, Cho-Jui Hsieh:
Ameliorate Spurious Correlations in Dataset Condensation. CoRR abs/2406.06609 (2024) - [i176]Ruochen Wang, Si Si, Felix Yu, Dorothea Wiesmann, Cho-Jui Hsieh, Inderjit S. Dhillon:
Large Language Models are Interpretable Learners. CoRR abs/2406.17224 (2024) - [i175]Xirui Li, Hengguang Zhou, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Cho-Jui Hsieh:
MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries? CoRR abs/2406.17806 (2024) - [i174]Ruochen Wang, Sohyun An, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, Cho-Jui Hsieh:
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts. CoRR abs/2407.00256 (2024) - [i173]Ruochen Wang, Ting Liu, Cho-Jui Hsieh, Boqing Gong:
On Discrete Prompt Optimization for Diffusion Models. CoRR abs/2407.01606 (2024) - [i172]Kuei-Chun Kao, Ruochen Wang, Cho-Jui Hsieh:
Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns? CoRR abs/2407.05134 (2024) - [i171]Jack He, Jianxing Zhao, Andrew Bai, Cho-Jui Hsieh:
Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Models. CoRR abs/2407.21159 (2024) - [i170]Yu-Hsiang Wang, Andrew Bai, Che-Ping Tsai, Cho-Jui Hsieh:
CLUE: Concept-Level Uncertainty Estimation for Large Language Models. CoRR abs/2409.03021 (2024) - [i169]Neha Prakriya, Jui-Nan Yen, Cho-Jui Hsieh, Jason Cong:
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review. CoRR abs/2409.06131 (2024) - [i168]Jui-Nan Yen, Si Si, Zhao Meng, Felix X. Yu, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh, Sanjiv Kumar:
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization. CoRR abs/2410.20625 (2024) - 2023
- [j28]Yuefeng Liang, Cho-Jui Hsieh, Thomas C. M. Lee:
Fast block-wise partitioning for extreme multi-label classification. Data Min. Knowl. Discov. 37(6): 2192-2215 (2023) - [j27]Achuta Kadambi, Celso de Melo, Cho-Jui Hsieh, Mani B. Srivastava, Stefano Soatto:
Incorporating physics into data-driven computer vision. Nat. Mac. Intell. 5(6): 572-580 (2023) - [j26]Liu Liu, Ji Liu, Cho-Jui Hsieh, Dacheng Tao:
Stochastically Controlled Compositional Gradient for Composition Problems. IEEE Trans. Neural Networks Learn. Syst. 34(2): 611-622 (2023) - [c192]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Training Meta-Surrogate Model for Transferable Adversarial Attack. AAAI 2023: 9516-9524 - [c191]Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh:
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract). AAAI 2023: 16292-16293 - [c190]Zixuan Ling, Xiaoqing Zheng, Jianhan Xu, Jinshu Lin, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang:
Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations. ACL (Findings) 2023: 11454-11465 - [c189]Jiong Zhang, Yau-Shian Wang, Wei-Cheng Chang, Wei Li, Jyun-Yu Jiang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Build Faster with Less: A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search. CIKM 2023: 4960-4966 - [c188]Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh:
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. CVPR 2023: 16323-16332 - [c187]Neha Prakriya, Yu Yang, Baharan Mirzasoleiman, Cho-Jui Hsieh, Jason Cong:
NeSSA: Near-Storage Data Selection for Accelerated Machine Learning Training. HotStorage 2023: 8-15 - [c186]Andrew Bai, Chih-Kuan Yeh, Neil Y. C. Lin, Pradeep Kumar Ravikumar, Cho-Jui Hsieh:
Concept Gradient: Concept-based Interpretation Without Linear Assumption. ICLR 2023 - [c185]Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh:
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories. ICLR 2023 - [c184]Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar:
Serving Graph Compression for Graph Neural Networks. ICLR 2023 - [c183]Yi Zeng, Zhouxing Shi, Ming Jin, Feiyang Kang, Lingjuan Lyu, Cho-Jui Hsieh, Ruoxi Jia:
Towards Robustness Certification Against Universal Perturbations. ICLR 2023 - [c182]Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu:
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. ICML 2023: 5616-5630 - [c181]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. ICML 2023: 6565-6590 - [c180]Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. ICML 2023: 34469-34490 - [c179]Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Robust Lipschitz Bandits to Adversarial Corruptions. NeurIPS 2023 - [c178]Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le:
Symbolic Discovery of Optimization Algorithms. NeurIPS 2023 - [c177]Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu:
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? NeurIPS 2023 - [c176]Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon:
A Computationally Efficient Sparsified Online Newton Method. NeurIPS 2023 - [c175]Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. NeurIPS 2023 - [c174]Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh:
Universality and Limitations of Prompt Tuning. NeurIPS 2023 - [c173]Jui-Nan Yen, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh:
Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization. NeurIPS 2023 - [c172]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Uncertainty Quantification for Extreme Classification. SIGIR 2023: 1649-1659 - [c171]Patrick H. Chen, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. WWW 2023: 3225-3235 - [i167]Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. CoRR abs/2302.01381 (2023) - [i166]Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Yao Liu, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le:
Symbolic Discovery of Optimization Algorithms. CoRR abs/2302.06675 (2023) - [i165]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Online Continuous Hyperparameter Optimization for Contextual Bandits. CoRR abs/2302.09440 (2023) - [i164]Yuanhao Xiong, Long Zhao, Boqing Gong, Ming-Hsuan Yang, Florian Schroff, Ting Liu, Cho-Jui Hsieh, Liangzhe Yuan:
Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding. CoRR abs/2303.16341 (2023) - [i163]Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh:
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories. CoRR abs/2304.13424 (2023) - [i162]Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu:
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. CoRR abs/2305.12349 (2023) - [i161]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Robust Lipschitz Bandits to Adversarial Corruptions. CoRR abs/2305.18543 (2023) - [i160]Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh:
Universality and Limitations of Prompt Tuning. CoRR abs/2305.18787 (2023) - [i159]Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, Cho-Jui Hsieh:
Red Teaming Language Model Detectors with Language Models. CoRR abs/2305.19713 (2023) - [i158]Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. CoRR abs/2305.20002 (2023) - [i157]Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Wei Wang:
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering. CoRR abs/2310.05007 (2023) - [i156]Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu:
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? CoRR abs/2310.07269 (2023) - [i155]Lucas Tecot, Cho-Jui Hsieh:
Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems. CoRR abs/2310.09468 (2023) - [i154]Liu Liu, Xuanqing Liu, Cho-Jui Hsieh, Dacheng Tao:
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function. CoRR abs/2310.11866 (2023) - [i153]Yuhang Li, Yihan Wang, Zhouxing Shi, Cho-Jui Hsieh:
Improving the Generation Quality of Watermarked Large Language Models via Word Importance Scoring. CoRR abs/2311.09668 (2023) - [i152]Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon:
A Computationally Efficient Sparsified Online Newton Method. CoRR abs/2311.10085 (2023) - [i151]Cho-Jui Hsieh, Si Si, Felix X. Yu, Inderjit S. Dhillon:
Automatic Engineering of Long Prompts. CoRR abs/2311.10117 (2023) - [i150]Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan:
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models. CoRR abs/2312.02429 (2023) - [i149]Tiejin Chen, Yuanpu Cao, Yujia Wang, Cho-Jui Hsieh, Jinghui Chen:
Federated Learning with Projected Trajectory Regularization. CoRR abs/2312.14380 (2023) - 2022
- [j25]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. Comput. Vis. Image Underst. 224: 103557 (2022) - [j24]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
On the Adversarial Robustness of Vision Transformers. Trans. Mach. Learn. Res. 2022 (2022) - [j23]Hojung Lee, Cho-Jui Hsieh, Jong-Seok Lee:
Local Critic Training for Model-Parallel Learning of Deep Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4424-4436 (2022) - [c170]Jianhan Xu, Cenyuan Zhang, Xiaoqing Zheng, Linyang Li, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang:
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples. ACL (Findings) 2022: 1694-1707 - [c169]Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang:
On the Sensitivity and Stability of Model Interpretations in NLP. ACL (1) 2022: 2631-2647 - [c168]Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh:
Improving the Adversarial Robustness of NLP Models by Information Bottleneck. ACL (Findings) 2022: 3588-3598 - [c167]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. AISTATS 2022: 7111-7123 - [c166]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
Robust Text CAPTCHAs Using Adversarial Examples. IEEE Big Data 2022: 1495-1504 - [c165]Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You:
Towards Efficient and Scalable Sharpness-Aware Minimization. CVPR 2022: 12350-12360 - [c164]Yuanhao Xiong, Cho-Jui Hsieh:
Learning to Learn with Smooth Regularization. ECCV (23) 2022: 550-565 - [c163]Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang:
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. EMNLP 2022: 6567-6584 - [c162]Jianhan Xu, Linyang Li, Jiping Zhang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang:
Weight Perturbation as Defense against Adversarial Word Substitutions. EMNLP (Findings) 2022: 7054-7063 - [c161]Xiangning Chen, Cho-Jui Hsieh, Boqing Gong:
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations. ICLR 2022 - [c160]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. ICLR 2022 - [c159]Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng:
Generalizing Few-Shot NAS with Gradient Matching. ICLR 2022 - [c158]Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You:
Concurrent Adversarial Learning for Large-Batch Training. ICLR 2022 - [c157]Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. ICLR 2022 - [c156]Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh:
Learning to Schedule Learning rate with Graph Neural Networks. ICLR 2022 - [c155]Huan Zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks. ICML 2022: 26591-26604 - [c154]Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee:
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks. ICPR 2022: 1287-1293 - [c153]Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, Cho-Jui Hsieh:
CAT: Customized Adversarial Training for Improved Robustness. IJCAI 2022: 673-679 - [c152]Hsiang-Fu Yu, Jiong Zhang, Wei-Cheng Chang, Jyun-Yu Jiang, Wei Li, Cho-Jui Hsieh:
PECOS: Prediction for Enormous and Correlated Output Spaces. KDD 2022: 4848-4849 - [c151]Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu:
The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022). KDD 2022: 4858-4859 - [c150]Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
Extreme Zero-Shot Learning for Extreme Text Classification. NAACL-HLT 2022: 5455-5468 - [c149]Qin Ding, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack:
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. NeurIPS 2022 - [c148]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
DC-BENCH: Dataset Condensation Benchmark. NeurIPS 2022 - [c147]Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces. NeurIPS 2022 - [c146]Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. NeurIPS 2022 - [c145]Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh:
Are AlphaZero-like Agents Robust to Adversarial Perturbations? NeurIPS 2022 - [c144]Yong Liu, Siqi Mai, Minhao Cheng, Xiangning Chen, Cho-Jui Hsieh, Yang You:
Random Sharpness-Aware Minimization. NeurIPS 2022 - [c143]Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh:
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. NeurIPS 2022 - [c142]Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh:
Efficient Non-Parametric Optimizer Search for Diverse Tasks. NeurIPS 2022 - [c141]Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
General Cutting Planes for Bound-Propagation-Based Neural Network Verification. NeurIPS 2022 - [c140]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification. SIGIR 2022: 1870-1874 - [i148]Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You:
Towards Efficient and Scalable Sharpness-Aware Minimization. CoRR abs/2203.02714 (2022) - [i147]Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. CoRR abs/2203.08961 (2022) - [i146]Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng:
Generalizing Few-Shot NAS with Gradient Matching. CoRR abs/2203.15207 (2022) - [i145]Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh:
Improving the Adversarial Robustness of NLP Models by Information Bottleneck. CoRR abs/2206.05511 (2022) - [i144]Patrick H. Chen, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. CoRR abs/2206.11408 (2022) - [i143]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
DC-BENCH: Dataset Condensation Benchmark. CoRR abs/2207.09639 (2022) - [i142]Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh:
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. CoRR abs/2207.09653 (2022) - [i141]Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
General Cutting Planes for Bound-Propagation-Based Neural Network Verification. CoRR abs/2208.05740 (2022) - [i140]Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar, Neil Y. C. Lin, Cho-Jui Hsieh:
Concept Gradient: Concept-based Interpretation Without Linear Assumption. CoRR abs/2208.14966 (2022) - [i139]Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh:
Efficient Non-Parametric Optimizer Search for Diverse Tasks. CoRR abs/2209.13575 (2022) - [i138]Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh:
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. CoRR abs/2210.07394 (2022) - [i137]Chenxi Gu, Chengsong Huang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh:
Watermarking Pre-trained Language Models with Backdooring. CoRR abs/2210.07543 (2022) - [i136]Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
End-to-End Learning to Index and Search in Large Output Spaces. CoRR abs/2210.08410 (2022) - [i135]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Uncertainty in Extreme Multi-label Classification. CoRR abs/2210.10160 (2022) - [i134]Andrew Bai, Cho-Jui Hsieh, Wendy Chi-wen Kan, Hsuan-Tien Lin:
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection. CoRR abs/2210.12231 (2022) - [i133]Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang:
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. CoRR abs/2210.12396 (2022) - [i132]Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix X. Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar:
Preserving In-Context Learning ability in Large Language Model Fine-tuning. CoRR abs/2211.00635 (2022) - [i131]Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh:
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning. CoRR abs/2211.02468 (2022) - [i130]Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh:
Are AlphaZero-like Agents Robust to Adversarial Perturbations? CoRR abs/2211.03769 (2022) - [i129]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. CoRR abs/2211.10586 (2022) - 2021
- [j22]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) - [c139]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 - [c138]Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das:
Self-Progressing Robust Training. AAAI 2021: 7107-7115 - [c137]Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh:
Multi-Proxy Wasserstein Classifier for Image Classification. AAAI 2021: 8618-8626 - [c136]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 - [c135]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 - [c134]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 - [c133]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 - [c132]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 - [c131]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. ICCV 2021: 3263-3272 - [c130]Yao Li, Martin Renqiang Min, Thomas C. M. Lee, Wenchao Yu, Erik Kruus, Wei Wang, Cho-Jui Hsieh:
Towards Robustness of Deep Neural Networks via Regularization. ICCV 2021: 7476-7485 - [c129]Ruochen Wang, Xiangning Chen, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh:
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving. ICCV 2021: 10357-10366 - [c128]Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh:
DrNAS: Dirichlet Neural Architecture Search. ICLR 2021 - [c127]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 - [c126]Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh:
Rethinking Architecture Selection in Differentiable NAS. ICLR 2021 - [c125]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 - [c124]Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh:
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary. ICLR 2021 - [c123]Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai:
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization. ICML 2021: 1760-1770 - [c122]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 - [c121]Sunipa Dev, Mehrnoosh Sameki, Jwala Dhamala, Cho-Jui Hsieh:
Measures and Best Practices for Responsible AI. KDD 2021: 4118 - [c120]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 - [c119]Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh:
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification. NeurIPS 2021: 13937-13949 - [c118]Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Label Disentanglement in Partition-based Extreme Multilabel Classification. NeurIPS 2021: 15359-15369 - [c117]Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio:
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding. NeurIPS 2021: 15816-15829 - [c116]Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
Fast Certified Robust Training with Short Warmup. NeurIPS 2021: 18335-18349 - [c115]Patrick H. Chen, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
DRONE: Data-aware Low-rank Compression for Large NLP Models. NeurIPS 2021: 29321-29334 - [c114]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 Neural Network Robustness Verification. NeurIPS 2021: 29909-29921 - [i128]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
Robust Text CAPTCHAs Using Adversarial Examples. CoRR abs/2101.02483 (2021) - [i127]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) - [i126]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) - [i125]Hojung Lee, Cho-Jui Hsieh, Jong-Seok Lee:
Local Critic Training for Model-Parallel Learning of Deep Neural Networks. CoRR abs/2102.01963 (2021) - [i124]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) - [i123]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) - [i122]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
On the Adversarial Robustness of Visual Transformers. CoRR abs/2103.15670 (2021) - [i121]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) - [i120]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) - [i119]Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang:
On the Faithfulness Measurements for Model Interpretations. CoRR abs/2104.08782 (2021) - [i118]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) - [i117]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) - [i116]Yao Li, Tongyi Tang, Cho-Jui Hsieh, Thomas C. M. Lee:
Detecting Adversarial Examples with Bayesian Neural Network. CoRR abs/2105.08620 (2021) - [i115]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) - [i114]Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You:
Concurrent Adversarial Learning for Large-Batch Training. CoRR abs/2106.00221 (2021) - [i113]Xiangning Chen, Cho-Jui Hsieh, Boqing Gong:
When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations. CoRR abs/2106.01548 (2021) - [i112]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) - [i111]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) - [i110]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. CoRR abs/2106.02978 (2021) - [i109]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) - [i108]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) - [i107]Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh:
Rethinking Architecture Selection in Differentiable NAS. CoRR abs/2108.04392 (2021) - [i106]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) - [i105]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) - [i104]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) - [i103]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Training Meta-Surrogate Model for Transferable Adversarial Attack. CoRR abs/2109.01983 (2021) - [i102]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Adversarial Attack across Datasets. CoRR abs/2110.07718 (2021) - [i101]Rulin Shao, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
How and When Adversarial Robustness Transfers in Knowledge Distillation? CoRR abs/2110.12072 (2021) - [i100]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) - [i99]Shanda Li, Xiangning Chen, Di He, Cho-Jui Hsieh:
Can Vision Transformers Perform Convolution? CoRR abs/2111.01353 (2021) - [i98]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) - [i97]Jaehui Hwang, Huan Zhang, Jun-Ho Choi, Cho-Jui Hsieh, Jong-Seok Lee:
Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial Attacks. CoRR abs/2112.07921 (2021) - [i96]Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
Extreme Zero-Shot Learning for Extreme Text Classification. CoRR abs/2112.08652 (2021) - 2020
- [j21]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) - [j20]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) - [j19]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) - [j18]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
- [j17]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) - [j16]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) - [j15]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]Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Neural Networks. NeurIPS 2019: 13009-13020 - [c64]Yang You, Jonathan Hseu, Chris Ying, James Demmel, Kurt Keutzer, Cho-Jui Hsieh:
Large-batch training for LSTM and beyond. SC 2019: 9:1-9:16 - [c63]Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael P. Friedlander:
Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization. SDM 2019: 280-288 - [i68]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. CoRR abs/1901.04684 (2019) - [i67]Yang You, Jonathan Hseu, Chris Ying, James Demmel, Kurt Keutzer, Cho-Jui Hsieh:
Large-Batch Training for LSTM and Beyond. CoRR abs/1901.08256 (2019) - [i66]Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, Pengchuan Zhang:
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks. CoRR abs/1902.08722 (2019) - [i65]Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh:
Robust Decision Trees Against Adversarial Examples. CoRR abs/1902.10660 (2019) - [i64]Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, Kai-Wei Chang:
Efficient Contextual Representation Learning Without Softmax Layer. CoRR abs/1902.11269 (2019) - [i63]Yang You, Jing Li, Jonathan Hseu, Xiaodan Song, James Demmel, Cho-Jui Hsieh:
Reducing BERT Pre-Training Time from 3 Days to 76 Minutes. CoRR abs/1904.00962 (2019) - [i62]Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee:
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks. CoRR abs/1904.06097 (2019) - [i61]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. CoRR abs/1905.07953 (2019) - [i60]Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack:
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. CoRR abs/1905.10630 (2019) - [i59]Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh:
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. CoRR abs/1905.12217 (2019) - [i58]Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh:
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise. CoRR abs/1906.02355 (2019) - [i57]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
ML-LOO: Detecting Adversarial Examples with Feature Attribution. CoRR abs/1906.03499 (2019) - [i56]Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane S. Boning, Cho-Jui Hsieh:
Robustness Verification of Tree-based Models. CoRR abs/1906.03849 (2019) - [i55]Lu Wang, Xuanqing Liu, Jinfeng Yi, Zhi-Hua Zhou, Cho-Jui Hsieh:
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective. CoRR abs/1906.03972 (2019) - [i54]Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Duane S. Boning, Cho-Jui Hsieh:
Towards Stable and Efficient Training of Verifiably Robust Neural Networks. CoRR abs/1906.06316 (2019) - [i53]Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho-Jui Hsieh, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Networks. CoRR abs/1906.07916 (2019) - [i52]Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang:
VisualBERT: A Simple and Performant Baseline for Vision and Language. CoRR abs/1908.03557 (2019) - [i51]Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack:
Temporal Collaborative Ranking Via Personalized Transformer. CoRR abs/1908.05435 (2019) - [i50]Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, Cho-Jui Hsieh:
Natural Adversarial Sentence Generation with Gradient-based Perturbation. CoRR abs/1909.04495 (2019) - [i49]Minhao Cheng, Simranjit Singh, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh:
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack. CoRR abs/1909.10773 (2019) - [i48]Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee:
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Imbalanced Data. CoRR abs/1910.01112 (2019) - [i47]Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh:
Learning to Learn by Zeroth-Order Oracle. CoRR abs/1910.09464 (2019) - [i46]Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh:
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. CoRR abs/1910.14147 (2019) - [i45]Huan Zhang, Minhao Cheng, Cho-Jui Hsieh:
Enhancing Certifiable Robustness via a Deep Model Ensemble. CoRR abs/1910.14655 (2019) - [i44]Xiaoyun Wang, Xuanqing Liu, Cho-Jui Hsieh:
GraphDefense: Towards Robust Graph Convolutional Networks. CoRR abs/1911.04429 (2019) - [i43]Patrick H. Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai:
Overcoming Catastrophic Forgetting by Generative Regularization. CoRR abs/1912.01238 (2019) - 2018
- [j14]Kai-Yang Chiang, Inderjit S. Dhillon, Cho-Jui Hsieh:
Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations. J. Mach. Learn. Res. 19: 76:1-76:35 (2018) - [c62]Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. AAAI 2018: 10-17 - [c61]Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh:
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning. ACL (1) 2018: 2587-2597 - [c60]Xuanqing Liu, Minhao Cheng, Huan Zhang, Cho-Jui Hsieh:
Towards Robust Neural Networks via Random Self-ensemble. ECCV (7) 2018: 381-397 - [c59]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel:
On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm. GlobalSIP 2018: 1159-1163 - [c58]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel:
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. ICLR (Poster) 2018 - [c57]Minhao Cheng, Ian Davidson, Cho-Jui Hsieh:
Extreme Learning to Rank via Low Rank Assumption. ICML 2018: 950-959 - [c56]Xuanqing Liu, Cho-Jui Hsieh:
Fast Variance Reduction Method with Stochastic Batch Size. ICML 2018: 3185-3194 - [c55]Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane S. Boning, Inderjit S. Dhillon:
Towards Fast Computation of Certified Robustness for ReLU Networks. ICML 2018: 5273-5282 - [c54]Liwei Wu, Cho-Jui Hsieh, James Sharpnack:
SQL-Rank: A Listwise Approach to Collaborative Ranking. ICML 2018: 5311-5320 - [c53]Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel, Kurt Keutzer:
ImageNet Training in Minutes. ICPP 2018: 1:1-1:10 - [c52]Yang You, James Demmel, Cho-Jui Hsieh, Richard W. Vuduc:
Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems. ICS 2018: 307-317 - [c51]Minhao Cheng, Cho-Jui Hsieh:
Distributed Primal-Dual Optimization for Non-uniformly Distributed Data. IJCAI 2018: 2028-2034 - [c50]Xiaoyun Wang, Chun-Ming Lai, Yu-Cheng Lin, Cho-Jui Hsieh, Shyhtsun Felix Wu, Hasan Cam:
Multiple Accounts Detection on Facebook Using Semi-Supervised Learning on Graphs. MILCOM 2018: 1-9 - [c49]Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang:
Learning Word Embeddings for Low-Resource Languages by PU Learning. NAACL-HLT 2018: 1024-1034 - [c48]Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel:
Efficient Neural Network Robustness Certification with General Activation Functions. NeurIPS 2018: 4944-4953 - [c47]Yao Li, Minhao Cheng, Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh:
Learning from Group Comparisons: Exploiting Higher Order Interactions. NeurIPS 2018: 4986-4995 - [c46]Patrick H. Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh:
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking. NeurIPS 2018: 11011-11021 - [c45]Jun Wang, Cho-Jui Hsieh, Daming Shi:
NLRR++: Scalable Subspace Clustering via Non-Convex Block Coordinate Descent. SDM 2018: 28-36 - [i42]Xiaoyun Wang, Chun-Ming Lai, Yunfeng Hong, Cho-Jui Hsieh, Shyhtsun Felix Wu:
Multiple Accounts Detection on Facebook Using Semi-Supervised Learning on Graphs. CoRR abs/1801.09838 (2018) - [i41]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel:
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. CoRR abs/1801.10578 (2018) - [i40]Liwei Wu, Cho-Jui Hsieh, James Sharpnack:
SQL-Rank: A Listwise Approach to Collaborative Ranking. CoRR abs/1803.00114 (2018) - [i39]Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, Cho-Jui Hsieh:
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples. CoRR abs/1803.01128 (2018) - [i38]Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane S. Boning, Inderjit S. Dhillon, Luca Daniel:
Towards Fast Computation of Certified Robustness for ReLU Networks. CoRR abs/1804.09699 (2018) - [i37]Yang You, James Demmel, Cho-Jui Hsieh, Richard W. Vuduc:
Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems. CoRR abs/1805.00569 (2018) - [i36]Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang:
LearningWord Embeddings for Low-resource Languages by PU Learning. CoRR abs/1805.03366 (2018) - [i35]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. CoRR abs/1805.11770 (2018) - [i34]Liu Liu, Minhao Cheng, Cho-Jui Hsieh, Dacheng Tao:
Stochastic Zeroth-order Optimization via Variance Reduction method. CoRR abs/1805.11811 (2018) - [i33]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. CoRR abs/1805.12316 (2018) - [i32]Patrick H. Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh:
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking. CoRR abs/1806.06950 (2018) - [i31]Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, Cho-Jui Hsieh:
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach. CoRR abs/1807.04457 (2018) - [i30]Xuanqing Liu, Cho-Jui Hsieh:
From Adversarial Training to Generative Adversarial Networks. CoRR abs/1807.10454 (2018) - [i29]Xuanqing Liu, Cho-Jui Hsieh:
Fast Variance Reduction Method with Stochastic Batch Size. CoRR abs/1808.02169 (2018) - [i28]Liu Liu, Ji Liu, Cho-Jui Hsieh, Dacheng Tao:
Stochastically Controlled Stochastic Gradient for the Convex and Non-convex Composition problem. CoRR abs/1809.02505 (2018) - [i27]Liu Liu, Xuanqing Liu, Cho-Jui Hsieh, Dacheng Tao:
Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient. CoRR abs/1809.09853 (2018) - [i26]Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh:
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network. CoRR abs/1810.01279 (2018) - [i25]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel:
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm. CoRR abs/1810.08640 (2018) - [i24]Xiaoyun Wang, Joe Eaton, Cho-Jui Hsieh, Shyhtsun Felix Wu:
Attack Graph Convolutional Networks by Adding Fake Nodes. CoRR abs/1810.10751 (2018) - [i23]Huan Zhang, Pengchuan Zhang, Cho-Jui Hsieh:
RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications. CoRR abs/1810.11783 (2018) - [i22]Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. CoRR abs/1810.12406 (2018) - [i21]Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel:
Efficient Neural Network Robustness Certification with General Activation Functions. CoRR abs/1811.00866 (2018) - [i20]Yuefeng Liang, Cho-Jui Hsieh, Thomas C. M. Lee:
Block-wise Partitioning for Extreme Multi-label Classification. CoRR abs/1811.01305 (2018) - [i19]Yao Li, Martin Renqiang Min, Wenchao Yu, Cho-Jui Hsieh, Thomas C. M. Lee, Erik Kruus:
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding. CoRR abs/1811.07950 (2018) - 2017
- [j13]Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Memory Efficient Kernel Approximation. J. Mach. Learn. Res. 18: 20:1-20:32 (2017) - [c44]Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon:
Rank Aggregation and Prediction with Item Features. AISTATS 2017: 748-756 - [c43]Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh:
ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models. AISec@CCS 2017: 15-26 - [c42]Huang Fang, Minhao Cheng, Cho-Jui Hsieh:
A Hyperplane-Based Algorithm for Semi-Supervised Dimension Reduction. ICDM 2017: 101-110 - [c41]Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh:
Gradient Boosted Decision Trees for High Dimensional Sparse Output. ICML 2017: 3182-3190 - [c40]Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh:
Computable Expert Knowledge in Computer Games. ICMLA 2017: 749-754 - [c39]Huang Fang, Zhen Zhang, Yiqun Shao, Cho-Jui Hsieh:
Improved Bounded Matrix Completion for Large-Scale Recommender Systems. IJCAI 2017: 1654-1660 - [c38]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines. KDD 2017: 245-254 - [c37]Liwei Wu, Cho-Jui Hsieh, James Sharpnack:
Large-scale Collaborative Ranking in Near-Linear Time. KDD 2017: 515-524 - [c36]Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li:
Scalable Demand-Aware Recommendation. NIPS 2017: 2412-2421 - [c35]Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu:
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent. NIPS 2017: 5330-5340 - [c34]Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon:
A Greedy Approach for Budgeted Maximum Inner Product Search. NIPS 2017: 5453-5462 - [i18]Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li:
Positive-Unlabeled Demand-Aware Recommendation. CoRR abs/1702.06347 (2017) - [i17]Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu:
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent. CoRR abs/1705.09056 (2017) - [i16]Huan Zhang, Si Si, Cho-Jui Hsieh:
GPU-acceleration for Large-scale Tree Boosting. CoRR abs/1706.08359 (2017) - [i15]Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh:
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models. CoRR abs/1708.03999 (2017) - [i14]Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee, Yuekai Sun:
An inexact subsampled proximal Newton-type method for large-scale machine learning. CoRR abs/1708.08552 (2017) - [i13]Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. CoRR abs/1709.04114 (2017) - [i12]Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel:
100-epoch ImageNet Training with AlexNet in 24 Minutes. CoRR abs/1709.05011 (2017) - [i11]Xuanqing Liu, Minhao Cheng, Huan Zhang, Cho-Jui Hsieh:
Towards Robust Neural Networks via Random Self-ensemble. CoRR abs/1712.00673 (2017) - [i10]Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh:
Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning. CoRR abs/1712.02051 (2017) - 2016
- [j12]Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon:
Nomadic Computing for Big Data Analytics. Computer 49(4): 52-60 (2016) - [j11]Fushing Hsieh, Kevin Fujii, Cho-Jui Hsieh:
Machine Learning Meliorates Computing and Robustness in Discrete Combinatorial Optimization Problems. Frontiers Appl. Math. Stat. 2: 20 (2016) - [c33]Huan Zhang, Cho-Jui Hsieh:
Fixing the Convergence Problems in Parallel Asynchronous Dual Coordinate Descent. ICDM 2016: 619-628 - [c32]Huan Zhang, Cho-Jui Hsieh, Venkatesh Akella:
HogWild++: A New Mechanism for Decentralized Asynchronous Stochastic Gradient Descent. ICDM 2016: 629-638 - [c31]Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon:
Robust Principal Component Analysis with Side Information. ICML 2016: 2291-2299 - [c30]Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Computationally Efficient Nyström Approximation using Fast Transforms. ICML 2016: 2655-2663 - [c29]Si Si, Kai-Yang Chiang, Cho-Jui Hsieh, Nikhil Rao, Inderjit S. Dhillon:
Goal-Directed Inductive Matrix Completion. KDD 2016: 1165-1174 - [c28]Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu:
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order. NIPS 2016: 3054-3062 - [c27]Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, James Demmel, Cho-Jui Hsieh:
Asynchronous Parallel Greedy Coordinate Descent. NIPS 2016: 4682-4690 - [i9]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Communication-Efficient Parallel Block Minimization for Kernel Machines. CoRR abs/1608.02010 (2016) - [i8]Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon:
A Greedy Approach for Budgeted Maximum Inner Product Search. CoRR abs/1610.03317 (2016) - 2015
- [c26]Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent. ICML 2015: 2370-2379 - [c25]Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon:
PU Learning for Matrix Completion. ICML 2015: 2445-2453 - [c24]Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent. NIPS 2015: 2368-2376 - [c23]Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon:
Matrix Completion with Noisy Side Information. NIPS 2015: 3447-3455 - [c22]Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon:
A Scalable Asynchronous Distributed Algorithm for Topic Modeling. WWW 2015: 1340-1350 - [i7]Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent. CoRR abs/1504.01365 (2015) - 2014
- [j10]Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, Ambuj Tewari:
Prediction and clustering in signed networks: a local to global perspective. J. Mach. Learn. Res. 15(1): 1177-1213 (2014) - [j9]Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar:
QUIC: quadratic approximation for sparse inverse covariance estimation. J. Mach. Learn. Res. 15(1): 2911-2947 (2014) - [j8]Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Parallel matrix factorization for recommender systems. Knowl. Inf. Syst. 41(3): 793-819 (2014) - [j7]Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S. V. N. Vishwanathan, Inderjit S. Dhillon:
NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion. Proc. VLDB Endow. 7(11): 975-986 (2014) - [c21]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
A Divide-and-Conquer Solver for Kernel Support Vector Machines. ICML 2014: 566-574 - [c20]Cho-Jui Hsieh, Peder A. Olsen:
Nuclear Norm Minimization via Active Subspace Selection. ICML 2014: 575-583 - [c19]Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Memory Efficient Kernel Approximation. ICML 2014: 701-709 - [c18]Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings. NIPS 2014: 1008-1016 - [c17]Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Stephen Becker, Peder A. Olsen:
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models. NIPS 2014: 2006-2014 - [c16]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Fast Prediction for Large-Scale Kernel Machines. NIPS 2014: 3689-3697 - [i6]Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon:
PU Learning for Matrix Completion. CoRR abs/1411.6081 (2014) - [i5]Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon:
A Scalable Asynchronous Distributed Algorithm for Topic Modeling. CoRR abs/1412.4986 (2014) - 2013
- [c15]Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Large Scale Distributed Sparse Precision Estimation. NIPS 2013: 584-592 - [c14]Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Russell A. Poldrack:
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables. NIPS 2013: 3165-3173 - [c13]Cho-Jui Hsieh, Mitul Tiwari, Deepak Agarwal, Xinyi (Lisa) Huang, Sam Shah:
Organizational overlap on social networks and its applications. WWW 2013: 571-582 - [i4]Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Ambuj Tewari, Inderjit S. Dhillon:
Prediction and Clustering in Signed Networks: A Local to Global Perspective. CoRR abs/1302.5145 (2013) - [i3]Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar:
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. CoRR abs/1306.3212 (2013) - [i2]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
A Divide-and-Conquer Solver for Kernel Support Vector Machines. CoRR abs/1311.0914 (2013) - [i1]Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S. V. N. Vishwanathan, Inderjit S. Dhillon:
NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion. CoRR abs/1312.0193 (2013) - 2012
- [j6]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large Linear Classification When Data Cannot Fit in Memory. ACM Trans. Knowl. Discov. Data 5(4): 23:1-23:23 (2012) - [c12]Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems. ICDM 2012: 765-774 - [c11]Cho-Jui Hsieh, Kai-Yang Chiang, Inderjit S. Dhillon:
Low rank modeling of signed networks. KDD 2012: 507-515 - [c10]Inderjit S. Dhillon, Cho-Jui Hsieh, Mátyás A. Sustik, Pradeep Ravikumar:
Sparse inverse covariance matrix estimation using quadratic approximation. MLSLP 2012 - [c9]Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Arindam Banerjee:
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation. NIPS 2012: 2339-2347 - 2011
- [c8]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large Linear Classification When Data Cannot Fit in Memory. IJCAI 2011: 2777-2782 - [c7]Cho-Jui Hsieh, Inderjit S. Dhillon:
Fast coordinate descent methods with variable selection for non-negative matrix factorization. KDD 2011: 1064-1072 - [c6]Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar:
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. NIPS 2011: 2330-2338 - 2010
- [j5]Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models. J. Mach. Learn. Res. 11: 815-848 (2010) - [j4]Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin:
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. J. Mach. Learn. Res. 11: 1471-1490 (2010) - [j3]Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin:
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification. J. Mach. Learn. Res. 11: 3183-3234 (2010) - [c5]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large linear classification when data cannot fit in memory. KDD 2010: 833-842
2000 – 2009
- 2009
- [c4]Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy. ACL/IJCNLP (2) 2009: 285-288 - [c3]Hung-Yi Lo, Kai-Wei Chang, Shang-Tse Chen, Tsung-Hsien Chiang, Chun-Sung Ferng, Cho-Jui Hsieh, Yi-Kuang Ko, Tsung-Ting Kuo, Hung-Che Lai, Ken-Yi Lin, Chia-Hsuan Wang, Hsiang-Fu Yu, Chih-Jen Lin, Hsuan-Tien Lin, Shou-De Lin:
An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes. KDD Cup 2009: 57-64 - 2008
- [j2]Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin:
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines. J. Mach. Learn. Res. 9: 1369-1398 (2008) - [j1]Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin:
LIBLINEAR: A Library for Large Linear Classification. J. Mach. Learn. Res. 9: 1871-1874 (2008) - [c2]Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan:
A dual coordinate descent method for large-scale linear SVM. ICML 2008: 408-415 - [c1]S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin:
A sequential dual method for large scale multi-class linear svms. KDD 2008: 408-416
Coauthor Index
[c207] [c205] [c197] [i190] [i189] [i187] [i180] [i179] [i175] [i174] [c188] [c158] [c153] [c144] [c142] [i142] [i139] [c138] [c129] [c128] [c126] [i114] [i107] [i105] [i98] [c113] [c109] [c104] [i91] [i81] [i71] [i69] [c84] [c75] [c70] [c63] [i50] [i49] [i45] [c60] [c57] [c51] [c47] [i39] [i34] [i31] [c42] [i11]
[c209] [c203] [c202] [i176] [i168] [c176] [c173] [c171] [i152] [i151] [c160] [c153] [c150] [c147] [i144] [i136] [i132] [c118] [c115] [i108] [i100] [i96] [c97] [i91] [i88] [i71] [c83] [c73] [i68] [j14] [c55] [i38] [j13] [c44] [c41] [c38] [c34] [j12] [c31] [c30] [c29] [c27] [i9] [i8] [c26] [c25] [c24] [c23] [c22] [i7] [j10] [j9] [j8] [j7] [c21] [c19] [c18] [c17] [c16] [i6] [i5] [c15] [c14] [i4] [i3] [i2] [i1] [c12] [c11] [c10] [c9] [c7] [c6]
Thomas C. M. Lee
aka: Thomas Chun Man Lee
aka: Thomas Chun Man Lee
Pradeep Ravikumar
aka: Pradeep Kumar Ravikumar
aka: Pradeep Kumar Ravikumar
James Sharpnack
aka: James L. Sharpnack
aka: James L. Sharpnack
[c210] [c194] [c193] [c189] [c182] [c180] [c172] [c171] [i162] [i158] [i157] [i150] [c160] [c152] [c150] [c147] [c140] [i144] [i136] [i135] [c118] [c115] [i108] [i100] [i96] [c108] [c97] [i88] [c83] [c82] [i59] [c49] [i36] [c34] [j12] [c27] [i8] [c26] [c22] [i7] [j8] [j7] [i5] [i1] [j6] [c12] [c8] [c5] [c3]
[j33] [c196] [i186] [i181] [c185] [i163] [c155] [c154] [c145] [c143] [c141] [i141] [i138] [i130] [c125] [c124] [c120] [c116] [c114] [i126] [i124] [i121] [i120] [i117] [i97] [j19] [c113] [c111] [c102] [c100] [c99] [c96] [c95] [c91] [c90] [i95] [i92] [i90] [i89] [i86] [i82] [i77] [i74] [i72] [c86] [c85] [c79] [c78] [c75] [c73] [c72] [c67] [c66] [i68] [i66] [i65] [i62] [i56] [i54] [i45] [c62] [c61] [c60] [c59] [c58] [c55] [c48] [i41] [i39] [i38] [i35] [i31] [i25] [i23] [i21] [c43] [c41] [c35] [i17] [i16] [i15] [i13] [i11] [i10] [c33] [c32] [c28]
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