Cho-Jui Hsieh

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 355

Top Venues

Publications

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 (2024)
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. CoRR (2024)
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 (2024)
Tong Xie, Haoyu Li, Andrew Bai, Cho-Jui Hsieh
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation. CoRR (2024)
Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly
Which Pretrain Samples to Rehearse when Finetuning Pretrained Models? CoRR (2024)
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)
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. ICML (2023)
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
Robust Lipschitz Bandits to Adversarial Corruptions. CoRR (2023)
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 (2023)
Yuefeng Liang, Cho-Jui Hsieh, Thomas C. M. Lee
Fast block-wise partitioning for extreme multi-label classification. Data Min. Knowl. Discov. 37 (6) (2023)
Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh
Universality and Limitations of Prompt Tuning. NeurIPS (2023)
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu
Uncertainty Quantification for Extreme Classification. SIGIR (2023)
Liu Liu, Ji Liu, Cho-Jui Hsieh, Dacheng Tao
Stochastically Controlled Compositional Gradient for Composition Problems. IEEE Trans. Neural Networks Learn. Syst. 34 (2) (2023)
Cho-Jui Hsieh, Si Si, Felix X. Yu, Inderjit S. Dhillon
Automatic Engineering of Long Prompts. CoRR (2023)
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)
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
Online Continuous Hyperparameter Optimization for Contextual Bandits. CoRR (2023)
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. CVPR (2023)
Fnu Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon
A Computationally Efficient Sparsified Online Newton Method. CoRR (2023)
Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee
Robust Lipschitz Bandits to Adversarial Corruptions. NeurIPS (2023)
Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh
Training Meta-Surrogate Model for Transferable Adversarial Attack. AAAI (2023)
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)
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)
Yuhang Li, Yihan Wang, Zhouxing Shi, Cho-Jui Hsieh
Improving the Generation Quality of Watermarked Large Language Models via Word Importance Scoring. CoRR (2023)
Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? NeurIPS (2023)
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 (2023)
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)
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)
Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar
Serving Graph Compression for Graph Neural Networks. ICLR (2023)
Tiejin Chen, Yuanpu Cao, Yujia Wang, Cho-Jui Hsieh, Jinghui Chen
Federated Learning with Projected Trajectory Regularization. CoRR (2023)
Fnu Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon
A Computationally Efficient Sparsified Online Newton Method. NeurIPS (2023)
Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract). AAAI (2023)
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)
Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, Cho-Jui Hsieh
Red Teaming Language Model Detectors with Language Models. CoRR (2023)
Yi Zeng, Zhouxing Shi, Ming Jin, Feiyang Kang, Lingjuan Lyu, Cho-Jui Hsieh, Ruoxi Jia
Towards Robustness Certification Against Universal Perturbations. ICLR (2023)
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 (2023)
Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories. CoRR (2023)
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 (2023)
Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? CoRR (2023)
Jui-Nan Yen, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh
Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization. NeurIPS (2023)
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 (2023)
Lucas Tecot, Cho-Jui Hsieh
Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems. CoRR (2023)
Liu Liu, Xuanqing Liu, Cho-Jui Hsieh, Dacheng Tao
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function. CoRR (2023)
Neha Prakriya, Yu Yang, Baharan Mirzasoleiman, Cho-Jui Hsieh, Jason Cong
NeSSA: Near-Storage Data Selection for Accelerated Machine Learning Training. HotStorage (2023)
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) (2023)
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)
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)
Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh
Universality and Limitations of Prompt Tuning. CoRR (2023)
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 (2023)
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 (2023)
Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh
On the Convergence of Certified Robust Training with Interval Bound Propagation. CoRR (2022)
Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh
Robust Text CAPTCHAs Using Adversarial Examples. Big Data (2022)
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)
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. AISTATS (2022)
Andrew Bai, Cho-Jui Hsieh, Wendy Chi-wen Kan, Hsuan-Tien Lin
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection. CoRR (2022)
Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. NeurIPS (2022)
Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang
On the Sensitivity and Stability of Model Interpretations in NLP. ACL (1) (2022)
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. CoRR (2022)
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)
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)
Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar, Neil Y. C. Lin, Cho-Jui Hsieh
Concept Gradient: Concept-based Interpretation Without Linear Assumption. CoRR (2022)
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)
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 (2022)
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)
Yong Liu, Siqi Mai, Minhao Cheng, Xiangning Chen, Cho-Jui Hsieh, Yang You
Random Sharpness-Aware Minimization. NeurIPS (2022)
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)
Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You
Concurrent Adversarial Learning for Large-Batch Training. ICLR (2022)
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)
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)
Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. CoRR (2022)
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh
DC-BENCH: Dataset Condensation Benchmark. NeurIPS (2022)
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. CoRR (2022)
Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh
Learning to Schedule Learning rate with Graph Neural Networks. ICLR (2022)
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)
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 (2022)
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh
DC-BENCH: Dataset Condensation Benchmark. CoRR (2022)
Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, Cho-Jui Hsieh
CAT: Customized Adversarial Training for Improved Robustness. IJCAI (2022)
Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng
Generalizing Few-Shot NAS with Gradient Matching. ICLR (2022)
Chenxi Gu, Chengsong Huang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh
Watermarking Pre-trained Language Models with Backdooring. CoRR (2022)
Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng
Generalizing Few-Shot NAS with Gradient Matching. CoRR (2022)
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)
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)
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 (2022)
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 (2022)
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)
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)
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 (2022)
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)
Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh
Efficient Non-Parametric Optimizer Search for Diverse Tasks. NeurIPS (2022)
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 (2022)
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)
Yuanhao Xiong, Cho-Jui Hsieh
Learning to Learn with Smooth Regularization. ECCV (23) (2022)
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu
Uncertainty in Extreme Multi-label Classification. CoRR (2022)
Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You
Towards Efficient and Scalable Sharpness-Aware Minimization. CoRR (2022)
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)
Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. CoRR (2022)
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)
Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You
Towards Efficient and Scalable Sharpness-Aware Minimization. CVPR (2022)
Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh
On the Convergence of Certified Robust Training with Interval Bound Propagation. ICLR (2022)
Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning. CoRR (2022)
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 (2022)

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Publication Activity (10 Years)

Top Topics

Top Venues

Cho-Jui Hsieh