| 2026 | ACL | Rethinking RL Evaluation: Can Benchmarks Truly Reveal Failures of RL Methods? | Zihan Chen, Yiming Zhang, Hengguang Zhou, Zenghui Ding, Yining Sun, Cho-Jui Hsieh |
| 2025 | EMNLP | QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models. | Kuei-Chun Kao, Hsu Tzu-Yin, Yunqi Hong, Ruochen Wang, Cho-Jui Hsieh |
| 2025 | ICLR | The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise. | Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Boqing Gong, Cho-Jui Hsieh, Minhao Cheng |
| 2025 | ICLR | Is Your Multimodal Language Model Oversensitive to Safe Queries? | Xirui Li, Hengguang Zhou, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Cho-Jui Hsieh |
| 2025 | ICLR | Large Language Models are Interpretable Learners. | Ruochen Wang, Si Si, Felix X. Yu, Dorothea Wiesmann Rothuizen, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2025 | ICLR | LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization. | Jui-Nan Yen, Si Si, Zhao Meng, Felix X. Yu, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh, Sanjiv Kumar |
| 2025 | ICML | SeedLoRA: A Fusion Approach to Efficient LLM Fine-Tuning. | Yong Liu, Di Fu, Shenggan Cheng, Zirui Zhu, Yang Luo, Minhao Cheng, Cho-Jui Hsieh, Yang You |
| 2025 | ICML | OR-Bench: An Over-Refusal Benchmark for Large Language Models. | Justin Cui, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh |
| 2025 | KDD | Matryoshka Model Learning for Improved Elastic Student Models. | Chetan Verma, Aditya Srinivas Timmaraju, Cho-Jui Hsieh, Suyash Damle, Ngot Bui, Yang Zhang, Wen Chen, Xin Liu, Prateek Jain, Inderjit S. Dhillon |
| 2025 | NAACL | An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning. | Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly |
| 2025 | WWW | UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification. | Siddhant Kharbanda, Devaansh Gupta, Gururaj K, Pankaj Malhotra, Amit Singh, Cho-Jui Hsieh, Rohit Babbar |
| 2025 | TACAS | Neural Network Verification with Branch-and-Bound for General Nonlinearities. | Zhouxing Shi, Qirui Jin, Zico Kolter, Suman Jana, Cho-Jui Hsieh, Huan Zhang |
| 2024 | ACL | MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering. | Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Wei Wang |
| 2024 | ACL | Automatic Engineering of Long Prompts. | Cho-Jui Hsieh, Si Si, Felix X. Yu, Inderjit S. Dhillon |
| 2024 | ACL | Defending LLMs against Jailbreaking Attacks via Backtranslation. | Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh |
| 2024 | ECCV | Understanding the Impact of Negative Prompts: When and How Do They Take Effect? | Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Boqing Gong, Cho-Jui Hsieh |
| 2024 | EMNLP | Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns? | Kuei-Chun Kao, Ruochen Wang, Cho-Jui Hsieh |
| 2024 | EMNLP | DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLMs Jailbreakers. | Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, Cho-Jui Hsieh |
| 2024 | EMNLP | UNICORN: A Unified Causal Video-Oriented Language-Modeling Framework for Temporal Video-Language Tasks. | Yuanhao Xiong, Yixin Nie, Haotian Liu, Boxin Wang, Jun Chen, Rong Jin, Cho-Jui Hsieh, Lorenzo Torresani, Jie Lei |
| 2024 | ICLR | Combining Axes Preconditioners through Kronecker Approximation for Deep Learning. | Sai Surya Duvvuri, Devvrit, Rohan Anil, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2024 | ICLR | Two-stage LLM Fine-tuning with Less Specialization and More Generalization. | Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix X. Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar |
| 2024 | ICLR | Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding. | Yuanhao Xiong, Long Zhao, Boqing Gong, Ming-Hsuan Yang, Florian Schroff, Ting Liu, Cho-Jui Hsieh, Liangzhe Yuan |
| 2024 | ICML | Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning. | Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun, Chien Feng, Cho-Jui Hsieh, Chun-Yi Lee |
| 2024 | ICML | Ameliorate Spurious Correlations in Dataset Condensation. | Justin Cui, Ruochen Wang, Yuanhao Xiong, Cho-Jui Hsieh |
| 2024 | ICML | On Discrete Prompt Optimization for Diffusion Models. | Ruochen Wang, Ting Liu, Cho-Jui Hsieh, Boqing Gong |
| 2024 | ICML | One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts. | Ruochen Wang, Sohyun An, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, Cho-Jui Hsieh |
| 2024 | ICML | Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation. | Lujie Yang, Hongkai Dai, Zhouxing Shi, Cho-Jui Hsieh, Russ Tedrake, Huan Zhang |
| 2024 | KDD | Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features. | Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar |
| 2024 | WWW | Entity Disambiguation with Extreme Multi-label Ranking. | Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu |
| 2024 | UAI | Low-rank Matrix Bandits with Heavy-tailed Rewards. | Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee |
| 2024 | WSDM | PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models. | Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan |
| 2023 | AAAI | Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract). | Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh |
| 2023 | AAAI | Training Meta-Surrogate Model for Transferable Adversarial Attack. | Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh |
| 2023 | ACL | Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations. | Zixuan Ling, Xiaoqing Zheng, Jianhan Xu, Jinshu Lin, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang |
| 2023 | CIKM | Build Faster with Less: A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search. | Jiong Zhang, Yau-Shian Wang, Wei-Cheng Chang, Wei Li, Jyun-Yu Jiang, Cho-Jui Hsieh, Hsiang-Fu Yu |
| 2023 | CVPR | FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. | Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix X. Yu, Cho-Jui Hsieh |
| 2023 | ICLR | Concept Gradient: Concept-based Interpretation Without Linear Assumption. | Andrew Bai, Chih-Kuan Yeh, Neil Y. C. Lin, Pradeep Kumar Ravikumar, Cho-Jui Hsieh |
| 2023 | ICLR | Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories. | Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh |
| 2023 | ICLR | Serving Graph Compression for Graph Neural Networks. | Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar |
| 2023 | ICLR | Towards Robustness Certification Against Universal Perturbations. | Yi Zeng, Zhouxing Shi, Ming Jin, Feiyang Kang, Lingjuan Lyu, Cho-Jui Hsieh, Ruoxi Jia |
| 2023 | ICML | PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. | Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu |
| 2023 | ICML | Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. | Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh |
| 2023 | ICML | Representer Point Selection for Explaining Regularized High-dimensional Models. | Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar |
| 2023 | WWW | FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. | Patrick H. Chen, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh |
| 2023 | SIGIR | Uncertainty Quantification for Extreme Classification. | Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu |
| 2022 | ACL | Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples. | Jianhan Xu, Cenyuan Zhang, Xiaoqing Zheng, Linyang Li, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang |
| 2022 | ACL | On the Sensitivity and Stability of Model Interpretations in NLP. | Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang |
| 2022 | ACL | Improving the Adversarial Robustness of NLP Models by Information Bottleneck. | Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh |
| 2022 | AISTATS | Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. | Qin Ding, Cho-Jui Hsieh, James Sharpnack |
| 2022 | CVPR | Towards Efficient and Scalable Sharpness-Aware Minimization. | Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You |
| 2022 | ECCV | Learning to Learn with Smooth Regularization. | Yuanhao Xiong, Cho-Jui Hsieh |
| 2022 | EMNLP | Weight Perturbation as Defense against Adversarial Word Substitutions. | Jianhan Xu, Linyang Li, Jiping Zhang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang |
| 2022 | EMNLP | ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. | Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang |
| 2022 | ICLR | When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations. | Xiangning Chen, Cho-Jui Hsieh, Boqing Gong |
| 2022 | ICLR | Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction. | Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon |
| 2022 | ICLR | Generalizing Few-Shot NAS with Gradient Matching. | Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng |
| 2022 | ICLR | Concurrent Adversarial Learning for Large-Batch Training. | Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You |
| 2022 | ICLR | On the Convergence of Certified Robust Training with Interval Bound Propagation. | Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh |
| 2022 | ICLR | Learning to Schedule Learning rate with Graph Neural Networks. | Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh |
| 2022 | ICML | A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks. | Huan Zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter |
| 2022 | ICPR | Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks. | Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee |
| 2022 | IJCAI | CAT: Customized Adversarial Training for Improved Robustness. | Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, Cho-Jui Hsieh |
| 2022 | KDD | The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022). | Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu |
| 2022 | KDD | PECOS: Prediction for Enormous and Correlated Output Spaces. | Hsiang-Fu Yu, Jiong Zhang, Wei-Cheng Chang, Jyun-Yu Jiang, Wei Li, Cho-Jui Hsieh |
| 2022 | NAACL | Extreme Zero-Shot Learning for Extreme Text Classification. | Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon |
| 2022 | SIGIR | Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification. | Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu |
| 2021 | AAAI | Self-Progressing Robust Training. | Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das |
| 2021 | AAAI | Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search. | Li-Cheng Lan, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh |
| 2021 | AAAI | Multi-Proxy Wasserstein Classifier for Image Classification. | Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh |
| 2021 | ACL | Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble. | Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang |
| 2021 | AISTATS | An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. | Qin Ding, Cho-Jui Hsieh, James Sharpnack |
| 2021 | CVPR | Robust and Accurate Object Detection via Adversarial Learning. | Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong |
| 2021 | EMNLP | Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution. | Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh |
| 2021 | EMNLP | On the Transferability of Adversarial Attacks against Neural Text Classifier. | Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang |
| 2021 | ICCV | Towards Robustness of Deep Neural Networks via Regularization. | Yao Li, Martin Renqiang Min, Thomas C. M. Lee, Wenchao Yu, Erik Kruus, Wei Wang, Cho-Jui Hsieh |
| 2021 | ICCV | RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection. | Yongming Rao, Benlin Liu, Yi Wei, Jiwen Lu, Cho-Jui Hsieh, Jie Zhou |
| 2021 | ICCV | RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving. | Ruochen Wang, Xiangning Chen, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh |
| 2021 | ICLR | DrNAS: Dirichlet Neural Architecture Search. | Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh |
| 2021 | ICLR | Evaluations and Methods for Explanation through Robustness Analysis. | Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh |
| 2021 | ICLR | Rethinking Architecture Selection in Differentiable NAS. | Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh |
| 2021 | ICLR | Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers. | Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, Cho-Jui Hsieh |
| 2021 | ICLR | Robust Reinforcement Learning on State Observations with Learned Optimal Adversary. | Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh |
| 2021 | ICML | Overcoming Catastrophic Forgetting by Bayesian Generative Regularization. | Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai |
| 2021 | KDD | Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). | Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu |
| 2021 | KDD | Measures and Best Practices for Responsible AI. | Sunipa Dev, Mehrnoosh Sameki, Jwala Dhamala, Cho-Jui Hsieh |
| 2021 | NAACL | Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation. | Chong Zhang, Jieyu Zhao, Huan Zhang, Kai-Wei Chang, Cho-Jui Hsieh |
| 2020 | AAAI | Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples. | Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, Cho-Jui Hsieh |
| 2020 | AAAI | ML-LOO: Detecting Adversarial Examples with Feature Attribution. | Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan |
| 2020 | ACCV | Adversarially Robust Deep Image Super-Resolution Using Entropy Regularization. | Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee |
| 2020 | ACL | What Does BERT with Vision Look At? | Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang |
| 2020 | ACL | Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples. | Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, Cho-Jui Hsieh, Minhao Cheng, Xuanjing Huang |
| 2020 | AISTATS | Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. | Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh |
| 2020 | CVPR | How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework. | Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh |
| 2020 | ECCV | MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation. | Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh |
| 2020 | ECCV | Improved Adversarial Training via Learned Optimizer. | Yuanhao Xiong, Cho-Jui Hsieh |
| 2020 | ICLR | Sign-OPT: A Query-Efficient Hard-label Adversarial Attack. | Minhao Cheng, Simranjit Singh, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh |
| 2020 | ICLR | Learning to Learn by Zeroth-Order Oracle. | Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh |
| 2020 | ICLR | Robustness Verification for Transformers. | Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh |
| 2020 | ICLR | Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. | Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh |
| 2020 | ICLR | MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. | Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang |
| 2020 | ICLR | Towards Stable and Efficient Training of Verifiably Robust Neural Networks. | Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane S. Boning, Cho-Jui Hsieh |
| 2020 | ICML | Stabilizing Differentiable Architecture Search via Perturbation-based Regularization. | Xiangning Chen, Cho-Jui Hsieh |
| 2020 | ICML | Learning to Encode Position for Transformer with Continuous Dynamical Model. | Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh |
| 2020 | ICML | On Lp-norm Robustness of Ensemble Decision Stumps and Trees. | Yihan Wang, Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh |
| 2020 | RecSys | SSE-PT: Sequential Recommendation Via Personalized Transformer. | Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack |
| 2020 | WWW | Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems. | Jyun-Yu Jiang, Patrick H. Chen, Cho-Jui Hsieh, Wei Wang |
| 2020 | WWW | Efficient Neural Interaction Function Search for Collaborative Filtering. | Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li, Cho-Jui Hsieh |
| 2019 | AAAI | AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks. | Chun-Chen Tu, Pai-Shun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Shin-Ming Cheng |
| 2019 | AAAI | RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications. | Huan Zhang, Pengchuan Zhang, Cho-Jui Hsieh |
| 2019 | ACL | On the Robustness of Self-Attentive Models. | Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, Cho-Jui Hsieh |
| 2019 | AISTATS | A Fast Sampling Algorithm for Maximum Inner Product Search. | Qin Ding, Hsiang-Fu Yu, Cho-Jui Hsieh |
| 2019 | AISTATS | Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables. | Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2019 | CVPR | Rob-GAN: Generator, Discriminator, and Adversarial Attacker. | Xuanqing Liu, Cho-Jui Hsieh |
| 2019 | EMNLP | MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model. | Yukun Ma, Patrick H. Chen, Cho-Jui Hsieh |
| 2019 | GECCO | GenAttack: practical black-box attacks with gradient-free optimization. | Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, Huan Zhang, Cho-Jui Hsieh, Mani B. Srivastava |
| 2019 | ICCV | Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks. | Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee |
| 2019 | ICDM | Fast LSTM Inference by Dynamic Decomposition on Cloud Systems. | Yang You, Yuxiong He, Samyam Rajbhandari, Wenhan Wang, Cho-Jui Hsieh, Kurt Keutzer, James Demmel |
| 2019 | ICLR | Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach. | Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh |
| 2019 | ICLR | Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. | Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh |
| 2019 | ICLR | Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network. | Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh |
| 2019 | ICLR | The Limitations of Adversarial Training and the Blind-Spot Attack. | Huan Zhang, Hongge Chen, Zhao Song, Duane S. Boning, Inderjit S. Dhillon, Cho-Jui Hsieh |
| 2019 | ICML | Robust Decision Trees Against Adversarial Examples. | Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh |
| 2019 | KDD | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. | Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh |
| 2019 | NAACL | Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent. | Minhao Cheng, Wei Wei, Cho-Jui Hsieh |
| 2019 | SC | Large-batch training for LSTM and beyond. | Yang You, Jonathan Hseu, Chris Ying, James Demmel, Kurt Keutzer, Cho-Jui Hsieh |
| 2019 | SDM | Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization. | Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael P. Friedlander |
| 2018 | AAAI | EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. | Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh |
| 2018 | ACL | Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning. | Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh |
| 2018 | ECCV | Towards Robust Neural Networks via Random Self-ensemble. | Xuanqing Liu, Minhao Cheng, Huan Zhang, Cho-Jui Hsieh |
| 2018 | ICLR | Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. | Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel |
| 2018 | ICML | Extreme Learning to Rank via Low Rank Assumption. | Minhao Cheng, Ian Davidson, Cho-Jui Hsieh |
| 2018 | ICML | Fast Variance Reduction Method with Stochastic Batch Size. | Xuanqing Liu, Cho-Jui Hsieh |
| 2018 | ICML | Towards Fast Computation of Certified Robustness for ReLU Networks. | Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane S. Boning, Inderjit S. Dhillon |
| 2018 | ICML | SQL-Rank: A Listwise Approach to Collaborative Ranking. | Liwei Wu, Cho-Jui Hsieh, James Sharpnack |
| 2018 | ICPP | ImageNet Training in Minutes. | Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel, Kurt Keutzer |
| 2018 | IJCAI | Distributed Primal-Dual Optimization for Non-uniformly Distributed Data. | Minhao Cheng, Cho-Jui Hsieh |
| 2018 | ICS | Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems. | Yang You, James Demmel, Cho-Jui Hsieh, Richard W. Vuduc |
| 2018 | NAACL | Learning Word Embeddings for Low-Resource Languages by PU Learning. | Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang |
| 2018 | SDM | NLRR++: Scalable Subspace Clustering via Non-Convex Block Coordinate Descent. | Jun Wang, Cho-Jui Hsieh, Daming Shi |
| 2017 | AISTATS | Rank Aggregation and Prediction with Item Features. | Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2017 | CCS | ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models. | Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh |
| 2017 | ICDM | A Hyperplane-Based Algorithm for Semi-Supervised Dimension Reduction. | Huang Fang, Minhao Cheng, Cho-Jui Hsieh |
| 2017 | ICML | Gradient Boosted Decision Trees for High Dimensional Sparse Output. | Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh |
| 2017 | ICMLA | Computable Expert Knowledge in Computer Games. | Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh |
| 2017 | IJCAI | Improved Bounded Matrix Completion for Large-Scale Recommender Systems. | Huang Fang, Zhen Zhang, Yiqun Shao, Cho-Jui Hsieh |
| 2017 | KDD | Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines. | Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon |
| 2017 | KDD | Large-scale Collaborative Ranking in Near-Linear Time. | Liwei Wu, Cho-Jui Hsieh, James Sharpnack |
| 2016 | ICDM | Fixing the Convergence Problems in Parallel Asynchronous Dual Coordinate Descent. | Huan Zhang, Cho-Jui Hsieh |
| 2016 | ICDM | HogWild++: A New Mechanism for Decentralized Asynchronous Stochastic Gradient Descent. | Huan Zhang, Cho-Jui Hsieh, Venkatesh Akella |
| 2016 | ICML | Robust Principal Component Analysis with Side Information. | Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2016 | ICML | Computationally Efficient Nystrm Approximation using Fast Transforms. | Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2016 | KDD | Goal-Directed Inductive Matrix Completion. | Si Si, Kai-Yang Chiang, Cho-Jui Hsieh, Nikhil Rao, Inderjit S. Dhillon |
| 2015 | ICML | PU Learning for Matrix Completion. | Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon |
| 2015 | ICML | PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent. | Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon |
| 2015 | WWW | A Scalable Asynchronous Distributed Algorithm for Topic Modeling. | Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon |
| 2014 | ICML | Nuclear Norm Minimization via Active Subspace Selection. | Cho-Jui Hsieh, Peder A. Olsen |
| 2014 | ICML | A Divide-and-Conquer Solver for Kernel Support Vector Machines. | Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon |
| 2014 | ICML | Memory Efficient Kernel Approximation. | Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2013 | WWW | Organizational overlap on social networks and its applications. | Cho-Jui Hsieh, Mitul Tiwari, Deepak Agarwal, Xinyi (Lisa) Huang, Sam Shah |
| 2012 | ICDM | Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems. | Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon |
| 2012 | KDD | Low rank modeling of signed networks. | Cho-Jui Hsieh, Kai-Yang Chiang, Inderjit S. Dhillon |
| 2011 | IJCAI | Large Linear Classification When Data Cannot Fit in Memory. | Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin |
| 2011 | KDD | Fast coordinate descent methods with variable selection for non-negative matrix factorization. | Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2010 | KDD | Large linear classification when data cannot fit in memory. | Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin |
| 2009 | ACL | Iterative Scaling and Coordinate Descent Methods for Maximum Entropy. | Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin |
| 2008 | ICML | A dual coordinate descent method for large-scale linear SVM. | Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan |
| 2008 | KDD | A sequential dual method for large scale multi-class linear svms. | S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin |