Yao Qin - RankMe

52 papers A* 13A 2Journal 30Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning? WACV Shuo Chen, Zhen Han, Bailan He, Jianzhe Liu, Mark Buckley, Yao Qin, Philip Torr, Volker Tresp, Jindong Gu
2025	A*	`conf`	Detecting Out-of-Distribution Through the Lens of Neural Collapse. CVPR Litian Liu, Yao Qin
2025	A*	`conf`	Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs. EMNLP Andong Hua, Kenan Tang, Chenhe Gu, Jindong Gu, Eric Wong, Yao Qin
2025	J	`jnl`	Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs. CoRR Andong Hua, Kenan Tang, Chenhe Gu, Jindong Gu, Eric Wong, Yao Qin
2025	J	`jnl`	Improving Adversarial Transferability in MLLMs via Dynamic Vision-Language Alignment Attack. CoRR Chenhe Gu, Jindong Gu, Andong Hua, Yao Qin
2025	A*	`conf`	NutriBench: A Dataset for Evaluating Large Language Models in Nutrition Estimation from Meal Descriptions. ICLR Mehak Preet Dhaliwal, Andong Hua, Laya Pullela, Ryan Burke, Yao Qin
2024	J	`jnl`	A Minimalist Prompt for Zero-Shot Policy Learning. CoRR Meng Song, Xuezhi Wang, Tanay Biradar, Yao Qin, Manmohan Chandraker
2024	J	`jnl`	Automated Adversarial Discovery for Safety Classifiers. CoRR Yash Kumar Lal, Preethi Lahoti, Aradhana Sinha, Yao Qin, Ananth Balashankar
2024	J	`jnl`	Conflict-Aware Adversarial Training. CoRR Zhiyu Xue, Haohan Wang, Yao Qin, Ramtin Pedarsani
2024	A*	`conf`	Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting. ICLR Xinlu Zhang, Shiyang Li, Xianjun Yang, Chenxin Tian, Yao Qin, Linda Ruth Petzold
2024	A*	`conf`	Fast Decision Boundary based Out-of-Distribution Detector. ICML Litian Liu, Yao Qin
2024	A	`conf`	Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective. AISTATS Bhagyashree Puranik, Ahmad Beirami, Yao Qin, Upamanyu Madhow
2024	A*	`conf`	Initialization Matters for Adversarial Transfer Learning. CVPR Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin
2024	J	`jnl`	NutriBench: A Dataset for Evaluating Large Language Models in Carbohydrate Estimation from Meal Descriptions. CoRR Andong Hua, Mehak Preet Dhaliwal, Ryan Burke, Yao Qin
2023	J	`jnl`	A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models. CoRR Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip H. S. Torr
2023	J	`jnl`	Detecting Out-of-Distribution Through the Lens of Neural Collapse. CoRR Litian Liu, Yao Qin
2023	A*	`conf`	Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. NeurIPS Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin
2023	J	`jnl`	Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. CoRR Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin
2023	J	`jnl`	Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting. CoRR Xinlu Zhang, Shiyang Li, Xianjun Yang, Chenxin Tian, Yao Qin, Linda Ruth Petzold
2023	J	`jnl`	Fast Decision Boundary based Out-of-Distribution Detector. CoRR Litian Liu, Yao Qin
2023	J	`jnl`	Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals. CoRR Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Jilin Chen, Ed H. Chi, Alex Beutel
2023	—	`conf`	Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation. EMNLP (Findings) Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Ed H. Chi, Jilin Chen, Alex Beutel
2023	J	`jnl`	Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning. CoRR Ananth Balashankar, Xiao Ma, Aradhana Sinha, Ahmad Beirami, Yao Qin, Jilin Chen, Alex Beutel
2023	J	`jnl`	Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective. CoRR Bhagyashree Puranik, Ahmad Beirami, Yao Qin, Upamanyu Madhow
2023	J	`jnl`	Initialization Matters for Adversarial Transfer Learning. CoRR Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin
2023	J	`jnl`	Towards Robust Prompts on Vision-Language Models. CoRR Jindong Gu, Ahmad Beirami, Xuezhi Wang, Alex Beutel, Philip H. S. Torr, Yao Qin
2023	J	`jnl`	Training Deep Boltzmann Networks with Sparse Ising Machines. CoRR Shaila Niazi, Navid Anjum Aadit, Masoud Mohseni, Shuvro Chowdhury, Yao Qin, Kerem Yunus Çamsari
2023	—	`conf`	What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. SaTML Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel
2023	J	`jnl`	What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. CoRR Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel
2022	—	`conf`	Are Vision Transformers Robust to Patch Perturbations? ECCV (12) Jindong Gu, Volker Tresp, Yao Qin
2022	—	`conf`	Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers. EMNLP (Findings) Jieyu Zhao, Xuezhi Wang, Yao Qin, Jilin Chen, Kai-Wei Chang
2022	J	`jnl`	Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers. CoRR Jieyu Zhao, Xuezhi Wang, Yao Qin, Jilin Chen, Kai-Wei Chang
2022	A*	`conf`	Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. NeurIPS Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang
2021	J	`jnl`	Are Vision Transformers Robust to Patch Perturbations? CoRR Jindong Gu, Volker Tresp, Yao Qin
2021	A*	`conf`	Improving Calibration through the Relationship with Adversarial Robustness. NeurIPS Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi
2021	J	`jnl`	Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. CoRR Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang
2020	—	`conf`	CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. EMNLP (1) Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi
2020	J	`jnl`	CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. CoRR Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi
2020	J	`jnl`	Deflecting Adversarial Attacks. CoRR Yao Qin, Nicholas Frosst, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton
2020	A*	`conf`	Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. ICLR Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton
2020	—	`—`	Detecting, Diagnosing, Deflecting and Designing Adversarial Attacks. Yao Qin
2020	J	`jnl`	Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. CoRR Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi
2019	J	`jnl`	Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. CoRR Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton
2019	A*	`conf`	Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. ICML Yao Qin, Nicholas Carlini, Garrison W. Cottrell, Ian J. Goodfellow, Colin Raffel
2019	J	`jnl`	Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. CoRR Yao Qin, Nicholas Carlini, Ian J. Goodfellow, Garrison W. Cottrell, Colin Raffel
2018	—	`conf`	Autofocus Layer for Semantic Segmentation. MICCAI (3) Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha, Jay Nanavati, Garrison W. Cottrell, Antonio Criminisi, Aditya V. Nori
2018	J	`jnl`	Autofocus Layer for Semantic Segmentation. CoRR Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha, Jay Nanavati, Garrison W. Cottrell, Antonio Criminisi, Aditya V. Nori
2018	J	`jnl`	Hierarchical Cellular Automata for Visual Saliency. Int. J. Comput. Vis. Yao Qin, Mengyang Feng, Huchuan Lu, Garrison W. Cottrell
2017	A*	`conf`	A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. IJCAI Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, Garrison W. Cottrell
2017	J	`jnl`	A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. CoRR Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, Garrison W. Cottrell
2017	J	`jnl`	Hierarchical Cellular Automata for Visual Saliency. CoRR Yao Qin, Mengyang Feng, Huchuan Lu, Garrison W. Cottrell
2015	A*	`conf`	Saliency detection via Cellular Automata. CVPR Yao Qin, Huchuan Lu, Yiqun Xu, He Wang