Scalable Demand-Aware Recommendation. - Jinfeng Yi; Cho-Jui Hsieh; Kush R. Varshney; Lijun Zhang 005; Yao Li 015 (NIPS, 2017)

Positive-Unlabeled Demand-Aware Recommendation. - Jinfeng Yi; Cho-Jui Hsieh; Kush R. Varshney; Lijun Zhang 005; Yao Li 015 (CoRR, 2017)

Learning from Group Comparisons: Exploiting Higher Order Interactions. - Yao Li 015; Minhao Cheng; Kevin Fujii; Fushing Hsieh; Cho-Jui Hsieh (NeurIPS, 2018)

Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding. - Yao Li 015; Martin Renqiang Min; Wenchao Yu; Cho-Jui Hsieh; Thomas C. M. Lee; Erik Kruus (CoRR, 2018)

Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network. - Xuanqing Liu; Yao Li 015; Chongruo Wu; Cho-Jui Hsieh (ICLR (Poster), 2019)

Uncertainty Quantification for High-Dimensional Sparse Nonparametric Additive Models. - Qi Gao; Randy C. S. Lai; Thomas C. M. Lee; Yao Li 015 (Technometrics, 2020)

Towards Robustness of Deep Neural Networks via Regularization. - Yao Li 015; Martin Renqiang Min; Thomas C. M. Lee; Wenchao Yu; Erik Kruus; Wei Wang 010; Cho-Jui Hsieh (ICCV, 2021)

Detecting Adversarial Examples with Bayesian Neural Network. - Yao Li 015; Tongyi Tang; Cho-Jui Hsieh; Thomas C. M. Lee (CoRR, 2021)

A Review of Adversarial Attack and Defense for Classification Methods. - Yao Li 015; Minhao Cheng; Cho-Jui Hsieh; Thomas C. M. Lee (CoRR, 2021)

ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. - Fan Yin; Yao Li 015; Cho-Jui Hsieh; Kai-Wei Chang (EMNLP, 2022)

You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model. - Shengkun Tang; Yaqing Wang 001; Zhenglun Kong; Tianchi Zhang 004; Yao Li 015; Caiwen Ding; Yanzhi Wang; Yi Liang; Dongkuan Xu (CVPR, 2023)

Accelerating Dataset Distillation via Model Augmentation. - Lei Zhang 197; Jie Zhang 081; Bowen Lei; Subhabrata Mukherjee; Xiang Pan 001; Bo Zhao; Caiwen Ding; Yao Li 015; Dongkuan Xu (CVPR, 2023)

DeeDiff: Dynamic Uncertainty-Aware Early Exiting for Accelerating Diffusion Model Generation. - Shengkun Tang; Yaqing Wang 001; Caiwen Ding; Yi Liang; Yao Li 015; Dongkuan Xu (CoRR, 2023)

AdaDiff: Accelerating Diffusion Models Through Step-Wise Adaptive Computation. - Shengkun Tang; Yaqing Wang 001; Caiwen Ding; Yi Liang; Yao Li 015; Dongkuan Xu (ECCV (79), 2024)

DALD: Improving Logits-based Detector without Logits from Black-box LLMs. - Cong Zeng; Shengkun Tang; Xianjun Yang; Yuanzhou Chen; Yiyou Sun; Zhiqiang Xu; Yao Li 015; Haifeng Chen; Wei Cheng 002; Dongkuan Xu (NeurIPS, 2024)

Uncovering Distortion Differences: A Study of Adversarial Attacks and Machine Discriminability. - Xiawei Wang; Yao Li 015; Cho-Jui Hsieh; Thomas C. M. Lee (IEEE Access, 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 015; Haifeng Chen; Wei Cheng 002; Dongkuan Xu (CoRR, 2024)

Defense Against Syntactic Textual Backdoor Attacks with Token Substitution. - Xinglin Li; Xianwen He; Yao Li 015; Minhao Cheng (CoRR, 2024)

Trusted Aggregation (TAG): Backdoor Defense in Federated Learning. - Joseph Lavond; Minhao Cheng; Yao Li 015 (Trans. Mach. Learn. Res., 2024)

Adversarial Examples Detection With Bayesian Neural Network. - Yao Li 015; Tongyi Tang; Cho-Jui Hsieh; Thomas C. M. Lee (IEEE Trans. Emerg. Top. Comput. Intell., 2024)