Saliency detection via Cellular Automata. - Yao Qin 001; Huchuan Lu; Yiqun Xu; He Wang (CVPR, 2015)

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. - Yao Qin 001; Dongjin Song; Haifeng Chen; Wei Cheng 002; Guofei Jiang; Garrison W. Cottrell (IJCAI, 2017)

Autofocus Layer for Semantic Segmentation. - Yao Qin 001; Konstantinos Kamnitsas; Siddharth Ancha; Jay Nanavati; Garrison W. Cottrell; Antonio Criminisi; Aditya V. Nori (MICCAI (3), 2018)

Hierarchical Cellular Automata for Visual Saliency. - Yao Qin 001; Mengyang Feng; Huchuan Lu; Garrison W. Cottrell (Int. J. Comput. Vis., 2018)

Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. - Yao Qin 001; Nicholas Carlini; Garrison W. Cottrell; Ian J. Goodfellow; Colin Raffel (ICML, 2019)

CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. - Tianlu Wang; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Kang Li; Jilin Chen; Alex Beutel; Ed H. Chi (EMNLP (1), 2020)

Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. - Yao Qin 001; Nicholas Frosst; Sara Sabour; Colin Raffel; Garrison W. Cottrell; Geoffrey E. Hinton (ICLR, 2020)

Deflecting Adversarial Attacks. - Yao Qin 001; Nicholas Frosst; Colin Raffel; Garrison W. Cottrell; Geoffrey E. Hinton (CoRR, 2020)

Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. - Yao Qin 001; Xuezhi Wang 002; Alex Beutel; Ed H. Chi (CoRR, 2020)

Detecting, Diagnosing, Deflecting and Designing Adversarial Attacks. - Yao Qin 001 (2020)

Improving Calibration through the Relationship with Adversarial Robustness. - Yao Qin 001; Xuezhi Wang 002; Alex Beutel; Ed H. Chi (NeurIPS, 2021)

Are Vision Transformers Robust to Patch Perturbations? - Jindong Gu; Volker Tresp; Yao Qin 001 (ECCV (12), 2022)

Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers. - Jieyu Zhao 001; Xuezhi Wang 002; Yao Qin 001; Jilin Chen; Kai-Wei Chang (EMNLP (Findings), 2022)

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. - Yao Qin 001; Chiyuan Zhang; Ting Chen; Balaji Lakshminarayanan; Alex Beutel; Xuezhi Wang 002 (NeurIPS, 2022)

Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation. - Ananth Balashankar; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Nithum Thain; Ed H. Chi; Jilin Chen; Alex Beutel (EMNLP (Findings), 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 001 (NeurIPS, 2023)

What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. - Yao Qin 001; Xuezhi Wang 002; Balaji Lakshminarayanan; Ed H. Chi; Alex Beutel (SaTML, 2023)

Training Deep Boltzmann Networks with Sparse Ising Machines. - Shaila Niazi; Navid Anjum Aadit; Masoud Mohseni; Shuvro Chowdhury; Yao Qin 001; Kerem Yunus Çamsari (CoRR, 2023)

Towards Robust Prompts on Vision-Language Models. - Jindong Gu; Ahmad Beirami; Xuezhi Wang 002; Alex Beutel; Philip H. S. Torr; Yao Qin 001 (CoRR, 2023)

Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals. - Ananth Balashankar; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Nithum Thain; Jilin Chen; Ed H. Chi; Alex Beutel (CoRR, 2023)

A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models. - Jindong Gu; Zhen Han 003; Shuo Chen 014; Ahmad Beirami; Bailan He; Gengyuan Zhang; Ruotong Liao; Yao Qin 001; Volker Tresp; Philip H. S. Torr (CoRR, 2023)

Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning. - Ananth Balashankar; Xiao Ma 010; Aradhana Sinha; Ahmad Beirami; Yao Qin 001; Jilin Chen; Alex Beutel (CoRR, 2023)

Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective. - Bhagyashree Puranik; Ahmad Beirami; Yao Qin 001; Upamanyu Madhow (AISTATS, 2024)

Initialization Matters for Adversarial Transfer Learning. - Andong Hua; Jindong Gu; Zhiyu Xue; Nicholas Carlini; Eric Wong 001; Yao Qin 001 (CVPR, 2024)

Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting. - Xinlu Zhang; Shiyang Li; Xianjun Yang; Chenxin Tian; Yao Qin 001; Linda Ruth Petzold (ICLR, 2024)

Fast Decision Boundary based Out-of-Distribution Detector. - Litian Liu; Yao Qin 001 (ICML, 2024)

A Minimalist Prompt for Zero-Shot Policy Learning. - Meng Song; Xuezhi Wang 002; Tanay Biradar; Yao Qin 001; Manmohan Chandraker (CoRR, 2024)

Automated Adversarial Discovery for Safety Classifiers. - Yash Kumar Lal; Preethi Lahoti; Aradhana Sinha; Yao Qin 001; Ananth Balashankar (CoRR, 2024)

NutriBench: A Dataset for Evaluating Large Language Models in Carbohydrate Estimation from Meal Descriptions. - Andong Hua; Mehak Preet Dhaliwal; Ryan Burke; Yao Qin 001 (CoRR, 2024)

Conflict-Aware Adversarial Training. - Zhiyu Xue; Haohan Wang; Yao Qin 001; Ramtin Pedarsani (CoRR, 2024)

Detecting Out-of-Distribution Through the Lens of Neural Collapse. - Litian Liu; Yao Qin 001 (CVPR, 2025)

NutriBench: A Dataset for Evaluating Large Language Models in Nutrition Estimation from Meal Descriptions. - Mehak Preet Dhaliwal; Andong Hua; Laya Pullela; Ryan Burke; Yao Qin 001 (ICLR, 2025)

Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning? - Shuo Chen 014; Zhen Han 003; Bailan He; Jianzhe Liu; Mark Buckley; Yao Qin 001; Philip Torr 001; Volker Tresp; Jindong Gu (WACV, 2025)

Improving Adversarial Transferability in MLLMs via Dynamic Vision-Language Alignment Attack. - Chenhe Gu; Jindong Gu; Andong Hua; Yao Qin 001 (CoRR, 2025)