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2020 – today
- 2024
[j32]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)- [j31]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) - [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) - [c206]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 - [c205]Cho-Jui Hsieh, Si Si, Felix Yu, Inderjit S. Dhillon:
Automatic Engineering of Long Prompts. ACL (Findings) 2024: 10672-10685 - [c204]Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh:
Defending LLMs against Jailbreaking Attacks via Backtranslation. ACL (Findings) 2024: 16031-16046 - [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 - [i191]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. CoRR abs/2401.07298 (2024) - [i190]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) - [i189]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) - [i188]Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly:
Which Pretrain Samples to Rehearse when Finetuning Pretrained Models? CoRR abs/2402.08096 (2024) - [i187]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) - [i186]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) - [i185]Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh:
Defending LLMs against Jailbreaking Attacks via Backtranslation. CoRR abs/2402.16459 (2024) - [i184]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) - [i183]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) - [i182]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Low-rank Matrix Bandits with Heavy-tailed Rewards. CoRR abs/2404.17709 (2024) - [i181]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) - [i180]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) - [i179]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) - [i178]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) - [i177]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) - [i176]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) - [i175]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) - [i174]Justin Cui, Ruochen Wang, Yuanhao Xiong, Cho-Jui Hsieh:
Ameliorate Spurious Correlations in Dataset Condensation. CoRR abs/2406.06609 (2024) - [i173]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) - [i172]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) - [i171]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) - [i170]Ruochen Wang, Ting Liu, Cho-Jui Hsieh, Boqing Gong:
On Discrete Prompt Optimization for Diffusion Models. CoRR abs/2407.01606 (2024) - [i169]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) - [i168]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) - 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]