cuSCNN: A Secure and Batch-Processing Framework for Privacy-Preserving Convolutional Neural Network Prediction on GPU. - Yanan Bai; Quanliang Liu; Wenyuan Wu; Yong Feng (Frontiers Comput. Neurosci., 2021)

Towards Long-term Fairness in Interactive Recommendation: A Maximum Entropy Reinforcement Learning Approach. - Xiaoyu Shi 001; Quanliang Liu; Hong Xie 004; Mingsheng Shang 001 (ICWS, 2023)

Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion. - Francis Ogoke; Quanliang Liu; Olabode Ajenifujah; Alexander Myers; Guadalupe Quirarte; Jack L. Beuth; Jonathan Malen; Amir Barati Farimani (CoRR, 2023)

Neighbor importance-aware graph collaborative filtering for item recommendation. - Qingxian Wang 001; Suqiang Wu; Yanan Bai; Quanliang Liu; Xiaoyu Shi 001 (Neurocomputing, 2023)

RTiSR: a review-driven time interval-aware sequential recommendation method. - Xiaoyu Shi 001; Quanliang Liu; Yanan Bai; Mingsheng Shang 001 (J. Big Data, 2023)

Hierarchical Reinforcement Learning for Long-term Fairness in Interactive Recommendation. - Chongjun Xia; Xiaoyu Shi 001; Hong Xie 004; Quanliang Liu; Mingsheng Shang 001 (ICWS, 2024)

Beyond designer's knowledge: Generating materials design hypotheses via large language models. - Quanliang Liu; Maciej P. Polak; So Yeon Kim; MD Al Amin Shuvo; Hrishikesh Shridhar Deodhar; Jeongsoo Han; Dane Morgan; Hyunseok Oh (CoRR, 2024)

Maximum Entropy Policy for Long-Term Fairness in Interactive Recommender Systems. - Xiaoyu Shi 001; Quanliang Liu; Hong Xie 004; Yanan Bai; Mingsheng Shang 001 (IEEE Trans. Serv. Comput., 2024)

Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning Approach. - Xiaoyu Shi 001; Quanliang Liu; Hong Xie 004; Di Wu 056; Bo Peng; Mingsheng Shang 001; Defu Lian (ACM Trans. Inf. Syst., 2024)