Hsieh, Cho-Jui

171 publications

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

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

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

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