Feddecay: Adapting to Data Heterogeneity in Federated Learning with Within-round Learning Rate Decay. Joseph Daniel Lavond, Minhao Cheng, and Yao Li. Under Review, 2024.

Multiple Instance Learning for Breast Cancer Histopathology Images. Taebin Kim, Benjamin C. Calhoun, Yao Li, Aatish Thennavan, Lisa A. Carey, W. Fraser Symmans, Melissa A. Troester, Charles M. Perou, and J.S. Marron. 2024.

Improving Logits-based Detector without Logits from Black-box LLMs. Cong Zeng, Shengkun Tang, Xianjun Yang, Yuanzhou Chen, Yiyou Sun, Zhiqiang Xu, Yao Li, Haifeng Chen, Wei Cheng, and Dongkuan Xu. In NeurIPS, 2024.

Image Analysis-based Identification of High Risk ER-Positive, HER2-Negative Breast Cancers. Dong Neuck Lee, Yao Li, Linnea T. Olsson, Alina M. Hamilton, Benjamin C. Calhoun, Katherine A. Hoaddley, J.S. Marron, Melissa A. Troester. In Breast Cancer Research, 2024.

Biased Dueling Bandits with Stochastic Delayed Feedback. Bongsoo Yi, Yue Kang, and Yao Li. In TMLR, 2024.

Uncovering Distortion Differences: A Study of Adversarial Attacks and Machine Discriminability. Xiawei Wang, Yao Li, Cho-Jui Hsieh, and Thomas C. M. Lee. In IEEE Access, 2024.

Stain SAN: Simultaneous Augmentation and Normalization for Histopathology Images. Taebin Kim, Yao Li, Benjamin C. Calhoun, Aatish Thennavan, Lisa A. Carey, W. Fraser Symmans, Melissa A. Troester, Charles M. Perou, and J.S. Marron. In JMI, 2024.

AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation. Shengkun Tang, Yaqing Wang, Caiwen Ding, Yi Liang, Yao Li, Dongkuan Xu. In ECCV, 2024.

Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images - Nevus & Melanoma. Yi Cui, Yao Li, Jayson R. Miedema, Sharon N. Edmiston, Sherif Farag, J. S. Marron, and Nancy E. Thomas. In Cancers, 2024.

Visual Intratumor Heterogeneity and Breast Tumor Progression. Yao Li, Sarah C. Van Alsten, Dong Neuck Lee, Taebin Kim, Benjamin C. Calhoun, Charles M. Perou, Sara E. Wobker, J.S. Marron, Katherine A. Hoadley, and Melissa A. Troester. In Cancers, 2024.

Adversarial Examples Detection With Bayesian Neural Network. Yao Li, Tongyi Tang, Cho-Jui Hsieh, Thomas C. M. Lee. In IEEE TETCI, 2024.

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You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model. Shengkun Tang, Yaqing Wang, Zhenglun Kong, Tianchi Zhang, Yao Li, Caiwen Ding, Yanzhi Wang, Yi Liang, Dongkuan Xu. In CVPR, 2023.

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Accelerating Dataset Distillation via Model Augmentation. Lei Zhang, Jie Zhang, Bowen Lei, Subhabrata Mukherjee, Xiang Pan, Bo Zhao, Caiwen Ding, Yao Li, Dongkuan Xu. In CVPR, 2023.

Code Highlight

ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang. In EMNLP, 2022.

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A Review of Adversarial Attack and Defense for Classification Methods. Yao Li, Minhao Cheng, Cho-Jui Hsieh, Thomas C. M. Lee. In The American Statistician, 2021.

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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. In ICCV, 2021.

Code Video

L-Arginine Supplementation in Severe Asthma. Shu-Yi Liao, Megan R Showalter, Angela L Linderholm, Lisa Franzi, Celeste Kivler , Yao Li, Michael R Sa, Zachary A Kons, Oliver Fiehn, Lihong Qi, Amir A Zeki, Nicholas J Kenyon. In JCI Insight, 2020.

Uncertainty Quantification for High-Dimensional Sparse Nonparametric Additive Models. Qi Gao, Randy C. S. Lai, Thomas C. M. Lee, Yao Li. In Technometrics, 2019.

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

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

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