Hsieh, Cho-Jui

175 publications

ICLR 2026 Self-Forcing++: Towards Minute-Scale High-Quality Video Generation Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, Cho-Jui Hsieh

NeurIPS 2025 Don’t Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models Sohyun An, Ruochen Wang, Tianyi Zhou, Cho-Jui Hsieh

ICLR 2025 Is Your Multimodal Language Model Oversensitive to Safe Queries? Xirui Li, Hengguang Zhou, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Cho-Jui Hsieh

ICLR 2025 Large Language Models Are Interpretable Learners Ruochen Wang, Si Si, Felix Yu, Dorothea Wiesmann Rothuizen, Cho-Jui Hsieh, Inderjit S Dhillon

ICLR 2025 LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization Jui-Nan Yen, Si Si, Zhao Meng, Felix Yu, Sai Surya Duvvuri, Inderjit S Dhillon, Cho-Jui Hsieh, Sanjiv Kumar

ICML 2025 OR-Bench: An Over-Refusal Benchmark for Large Language Models Justin Cui, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh

NeurIPS 2025 On the Loss of Context Awareness in General Instruction Fine-Tuning Yihan Wang, Andrew Bai, Nanyun Peng, Cho-Jui Hsieh

ICML 2025 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

TMLR 2025 SoundnessBench: A Soundness Benchmark for Neural Network Verifiers Xingjian Zhou, Keyi Shen, Andy Xu, Hongji Xu, Cho-Jui Hsieh, Huan Zhang, Zhouxing Shi

NeurIPS 2025 Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning Yong Liu, Zirui Zhu, Chaoyu Gong, Minhao Cheng, Cho-Jui Hsieh, Yang You

ICLR 2025 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

NeurIPS 2025 Unlabeled Data Improves Fine-Grained Image Zero-Shot Classification with Multimodal LLMs Yunqi Hong, Sohyun An, Andrew Bai, Neil Lin, Cho-Jui Hsieh

ICML 2024 Ameliorate Spurious Correlations in Dataset Condensation Justin Cui, Ruochen Wang, Yuanhao Xiong, Cho-Jui Hsieh

ICLR 2024 Combining Axes Preconditioners Through Kronecker Approximation for Deep Learning Sai Surya Duvvuri, Fnu Devvrit, Rohan Anil, Cho-Jui Hsieh, Inderjit S Dhillon

TMLR 2024 Data Attribution for Diffusion Models: Timestep-Induced Bias in Influence Estimation Tong Xie, Haoyu Li, Andrew Bai, Cho-Jui Hsieh

ICML 2024 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

UAI 2024 Low-Rank Matrix Bandits with Heavy-Tailed Rewards Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee

ICML 2024 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

ICML 2024 On Discrete Prompt Optimization for Diffusion Models Ruochen Wang, Ting Liu, Cho-Jui Hsieh, Boqing Gong

ICML 2024 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

TMLR 2024 Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits Yue Kang, Cho-Jui Hsieh, Thomas Lee

ICLR 2024 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

ICLR 2024 Two-Stage LLM Fine-Tuning with Less Specialization and More Generalization Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar

ECCV 2024 When and How Do Negative Prompts Take Effect? Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Boqing Gong, Cho-Jui Hsieh

NeurIPS 2023 A Computationally Efficient Sparsified Online Newton Method Fnu Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon

NeurIPSW 2023 AnchMark: Anchor-Contrastive Watermarking vs GenAI-Based Image Modifications Minzhou Pan, Yi Zeng, Xue Lin, Ning Yu, Cho-Jui Hsieh, Ruoxi Jia

NeurIPS 2023 Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization Jui-Nan Yen, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh

ICLR 2023 Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh

ICLR 2023 Concept Gradient: Concept-Based Interpretation Without Linear Assumption Andrew Bai, Chih-Kuan Yeh, Neil Y.C. Lin, Pradeep Kumar Ravikumar, Cho-Jui Hsieh

NeurIPS 2023 Effective Robustness Against Natural Distribution Shifts for Models with Different Training Data Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin

CVPR 2023 FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh

AAAI 2023 Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract) Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh

ICML 2023 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

NeurIPSW 2023 Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems Lucas Tecot, Cho-Jui Hsieh

ICML 2023 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

NeurIPS 2023 Robust Lipschitz Bandits to Adversarial Corruptions Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee

ICML 2023 Scaling up Dataset Distillation to ImageNet-1k with Constant Memory Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh

ICLR 2023 Serving Graph Compression for Graph Neural Networks Si Si, Felix Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar

NeurIPS 2023 Symbolic Discovery of Optimization Algorithms Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V Le

ICLR 2023 Towards Robustness Certification Against Universal Perturbations Yi Zeng, Zhouxing Shi, Ming Jin, Feiyang Kang, Lingjuan Lyu, Cho-Jui Hsieh, Ruoxi Jia

AAAI 2023 Training Meta-Surrogate Model for Transferable Adversarial Attack Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh

NeurIPSW 2023 Two-Stage LLM Fine-Tuning with Less Specialization and More Generalization Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar

NeurIPS 2023 Universality and Limitations of Prompt Tuning Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh

NeurIPS 2023 Why Does Sharpness-Aware Minimization Generalize Better than SGD? Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu

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

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

NeurIPS 2022 Are AlphaZero-like Agents Robust to Adversarial Perturbations? Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh

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

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

NeurIPS 2022 DC-BENCH: Dataset Condensation Benchmark Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh

NeurIPS 2022 ELIAS: End-to-End Learning to Index and Search in Large Output Spaces Nilesh Gupta, Patrick Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon

NeurIPS 2022 Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee

NeurIPS 2022 Efficient Non-Parametric Optimizer Search for Diverse Tasks Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh

NeurIPS 2022 Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh

NeurIPSW 2022 Evaluating Worst Case Adversarial Weather Perturbations Robustness Yihan Wang, Yunhao Ba, Howard Chenyang Zhang, Huan Zhang, Achuta Kadambi, Stefano Soatto, Alex Wong, Cho-Jui Hsieh

NeurIPSW 2022 FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh

NeurIPS 2022 General Cutting Planes for Bound-Propagation-Based Neural Network Verification Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter

ICLR 2022 Generalizing Few-Shot NAS with Gradient Matching Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng

ECCV 2022 Learning to Learn with Smooth Regularization Yuanhao Xiong, Cho-Jui Hsieh

ICLR 2022 Learning to Schedule Learning Rate with Graph Neural Networks Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh

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

TMLR 2022 On the Adversarial Robustness of Vision Transformers Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh

NeurIPSW 2022 On the Adversarial Robustness of Vision Transformers Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh

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

NeurIPS 2022 Random Sharpness-Aware Minimization Yong Liu, Siqi Mai, Minhao Cheng, Xiangning Chen, Cho-Jui Hsieh, Yang You

NeurIPS 2022 Syndicated Bandits: A Framework for Auto Tuning Hyper-Parameters in Contextual Bandit Algorithms Qin Ding, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack

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

ICLR 2022 When Vision Transformers Outperform ResNets Without Pre-Training or Strong Data Augmentations Xiangning Chen, Cho-Jui Hsieh, Boqing Gong

AISTATS 2021 An Efficient Algorithm for Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling Qin Ding, Cho-Jui Hsieh, James Sharpnack

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

ICMLW 2021 Beta-CROWN: Efficient Bound Propagation with Per-Neuron Split Constraints for Neural Network Robustness Verification Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J Zico Kolter

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

ICLR 2021 DrNAS: Dirichlet Neural Architecture Search Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh

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

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

NeurIPS 2021 Fast Certified Robust Training with Short Warmup Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh

ICMLW 2021 Fast Certified Robust Training with Short Warmup Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh

ICLR 2021 Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, Cho-Jui Hsieh

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

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

AAAI 2021 Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search Li-Cheng Lan, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh

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

ICML 2021 Overcoming Catastrophic Forgetting by Bayesian Generative Regularization Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai

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

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

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

ICLR 2021 Robust Reinforcement Learning on State Observations with Learned Optimal Adversary Huan Zhang, Hongge Chen, Duane S Boning, Cho-Jui Hsieh

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

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

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

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

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

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

AISTATS 2020 Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh

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

CVPR 2020 How Does Noise Help Robustness? Explanation and Exploration Under the Neural SDE Framework Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh

ECCV 2020 Improved Adversarial Training via Learned Optimizer Yuanhao Xiong, Cho-Jui Hsieh

ICLR 2020 Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh

ICML 2020 Learning to Encode Position for Transformer with Continuous Dynamical Model Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, Cho-Jui Hsieh

ICLR 2020 Learning to Learn by Zeroth-Order Oracle Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh

ICLR 2020 MACER: Attack-Free and Scalable Robust Training via Maximizing Certified Radius Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang

AAAI 2020 ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan

ECCV 2020 MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh

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

ICML 2020 On Lp-Norm Robustness of Ensemble Decision Stumps and Trees Yihan Wang, Huan Zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh

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

NeurIPS 2020 Robust Deep Reinforcement Learning Against Adversarial Perturbations on State Observations Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane Boning, Cho-Jui Hsieh

ICLR 2020 Robustness Verification for Transformers Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh

AAAI 2020 Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, Cho-Jui Hsieh

ICLR 2020 Sign-OPT: A Query-Efficient Hard-Label Adversarial Attack Minhao Cheng, Simranjit Singh, Patrick Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh

MLJ 2020 Spanning Attack: Reinforce Black-Box Attacks with Unlabeled Data Lu Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Yuan Jiang

ICML 2020 Stabilizing Differentiable Architecture Search via Perturbation-Based Regularization Xiangning Chen, Cho-Jui Hsieh

ICLR 2020 Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane Boning, Cho-Jui Hsieh

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

AISTATS 2019 A Fast Sampling Algorithm for Maximum Inner Product Search Qin Ding, Hsiang-Fu Yu, Cho-Jui Hsieh

NeurIPS 2019 A Unified Framework for Data Poisoning Attack to Graph-Based Semi-Supervised Learning Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh

ICLR 2019 Adv-BNN: Improved Adversarial Defense Through Robust Bayesian Neural Network Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh

AAAI 2019 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

NeurIPS 2019 Convergence of Adversarial Training in Overparametrized Neural Networks Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason Lee

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

ICLR 2019 Learning to Screen for Fast SoftMax Inference on Large Vocabulary Neural Networks Patrick Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh

AISTATS 2019 Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon

ICLR 2019 Query-Efficient Hard-Label Black-Box Attack: An Optimization-Based Approach Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, JinFeng Yi, Cho-Jui Hsieh

AAAI 2019 RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications Huan Zhang, Pengchuan Zhang, Cho-Jui Hsieh

CVPR 2019 Rob-GAN: Generator, Discriminator, and Adversarial Attacker Xuanqing Liu, Cho-Jui Hsieh

ICML 2019 Robust Decision Trees Against Adversarial Examples Hongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsieh

NeurIPS 2019 Robustness Verification of Tree-Based Models Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane Boning, Cho-Jui Hsieh

NeurIPS 2019 Stochastic Shared Embeddings: Data-Driven Regularization of Embedding Layers Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L Sharpnack

ICLR 2019 The Limitations of Adversarial Training and the Blind-Spot Attack Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S. Dhillon, Cho-Jui Hsieh

IJCAI 2018 Distributed Primal-Dual Optimization for Non-Uniformly Distributed Data Minhao Cheng, Cho-Jui Hsieh

AAAI 2018 EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh

NeurIPS 2018 Efficient Neural Network Robustness Certification with General Activation Functions Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel

ICLR 2018 Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel

ICML 2018 Extreme Learning to Rank via Low Rank Assumption Minhao Cheng, Ian Davidson, Cho-Jui Hsieh

ICML 2018 Fast Variance Reduction Method with Stochastic Batch Size Xuanqing Liu, Cho-Jui Hsieh

NeurIPS 2018 GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking Patrick Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh

NeurIPS 2018 Learning from Group Comparisons: Exploiting Higher Order Interactions Yao Li, Minhao Cheng, Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh

ICML 2018 SQL-Rank: A Listwise Approach to Collaborative Ranking Liwei Wu, Cho-Jui Hsieh, James Sharpnack

ICML 2018 Towards Fast Computation of Certified Robustness for ReLU Networks Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, Inderjit Dhillon

ECCV 2018 Towards Robust Neural Networks via Random Self-Ensemble Xuanqing Liu, Minhao Cheng, Huan Zhang, Cho-Jui Hsieh

JMLR 2018 Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations Kai-Yang Chiang, Inderjit S. Dhillon, Cho-Jui Hsieh

NeurIPS 2017 A Greedy Approach for Budgeted Maximum Inner Product Search Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S Dhillon

NeurIPS 2017 Can Decentralized Algorithms Outperform Centralized Algorithms? a Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu

ICML 2017 Gradient Boosted Decision Trees for High Dimensional Sparse Output Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh

IJCAI 2017 Improved Bounded Matrix Completion for Large-Scale Recommender Systems Huang Fang, Zhen Zhang, Yiqun Shao, Cho-Jui Hsieh

JMLR 2017 Memory Efficient Kernel Approximation Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon

AISTATS 2017 Rank Aggregation and Prediction with Item Features Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon

NeurIPS 2017 Scalable Demand-Aware Recommendation Jinfeng Yi, Cho-Jui Hsieh, Kush R Varshney, Lijun Zhang, Yao Li

NeurIPS 2016 A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu

NeurIPS 2016 Asynchronous Parallel Greedy Coordinate Descent Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S Dhillon, James Demmel, Cho-Jui Hsieh

ICML 2016 Computationally Efficient Nyström Approximation Using Fast Transforms Si Si, Cho-Jui Hsieh, Inderjit Dhillon

ICML 2016 Robust Principal Component Analysis with Side Information Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon

NeurIPS 2015 Matrix Completion with Noisy Side Information Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S Dhillon

ICML 2015 PASSCoDe: Parallel ASynchronous Stochastic Dual Co-Ordinate Descent Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon

ICML 2015 PU Learning for Matrix Completion Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon

NeurIPS 2015 Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon

ICML 2014 A Divide-and-Conquer Solver for Kernel Support Vector Machines Cho-Jui Hsieh, Si Si, Inderjit Dhillon

NeurIPS 2014 Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods Under High-Dimensional Settings Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon

NeurIPS 2014 Fast Prediction for Large-Scale Kernel Machines Cho-Jui Hsieh, Si Si, Inderjit S Dhillon

ICML 2014 Memory Efficient Kernel Approximation Si Si, Cho-Jui Hsieh, Inderjit Dhillon

ICML 2014 Nuclear Norm Minimization via Active Subspace Selection Cho-Jui Hsieh, Peder Olsen

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

NeurIPS 2014 QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models Cho-Jui Hsieh, Inderjit S Dhillon, Pradeep K Ravikumar, Stephen Becker, Peder A. Olsen

JMLR 2014 QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar

NeurIPS 2013 BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, Matyas A Sustik, Inderjit S Dhillon, Pradeep K Ravikumar, Russell Poldrack

NeurIPS 2013 Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon

NeurIPS 2012 A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation Cho-jui Hsieh, Arindam Banerjee, Inderjit S. Dhillon, Pradeep K. Ravikumar

IJCAI 2011 Large Linear Classification When Data Cannot Fit in Memory Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin

NeurIPS 2011 Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation Cho-jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Mátyás A. Sustik

JMLR 2010 A Comparison of Optimization Methods and Software for Large-Scale L1-Regularized Linear Classification Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin

JMLR 2010 Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin

JMLR 2010 Training and Testing Low-Degree Polynomial Data Mappings via Linear SVM Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin

ICML 2008 A Dual Coordinate Descent Method for Large-Scale Linear SVM Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan

JMLR 2008 Coordinate Descent Method for Large-Scale L2-Loss Linear Support Vector Machines Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin

MLOSS 2008 LIBLINEAR: A Library for Large Linear Classification Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin