Device-To-Device Assisted Video Transmission. - Yichao Shen; Wenwen Zhou; Peizhi Wu; Laura Toni; Pamela C. Cosman; Laurence B. Milstein (PV, 2013)

I/Q Imbalance Estimation for Direct Conversion Receivers by Jointly Using the Existing Preambles and Blind Estimation. - Peizhi Wu; Prasad Shamain; Sayantan Choudhury; Klaus Doppler (GLOBECOM, 2015)

Resource Allocation for Multicarrier D2D Video Transmission Based on Exact Symbol Error Rate. - Peizhi Wu; Pamela C. Cosman; Laurence B. Milstein (GLOBECOM, 2016)

Performance Analysis and Resource Allocation for Device-to-Device Video Transmission. - Peizhi Wu (2016)

Resource Allocation for Multicarrier Device-to-Device Video Transmission: Symbol Error Rate Analysis and Algorithm Design. - Peizhi Wu; Pamela C. Cosman; Laurence B. Milstein (IEEE Trans. Commun., 2017)

Subcarrier Assignment and Power Allocation for Device-to-Device Video Transmission in Rayleigh Fading Channels. - Peizhi Wu; Pamela C. Cosman; Laurence B. Milstein (IEEE Trans. Wirel. Commun., 2017)

Neural Framework for Joint Evolution Modeling of User Feedback and Social Links in Dynamic Social Networks. - Peizhi Wu; Yi Tu; Xiaojie Yuan; Adam Jatowt; Zhenglu Yang (IJCAI, 2018)

Deep Modeling of the Evolution of User Preferences and Item Attributes in Dynamic Social Networks. - Peizhi Wu; Yi Tu; Zhenglu Yang; Adam Jatowt; Masato Odagaki (WWW (Companion Volume), 2018)

dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation. - Le Huang; Han Zhang 017; Peizhi Wu; Sarah Entwistle; Xueqiong Li; Tanner Yohe; Haidong Yi; Zhenglu Yang; Yanbin Yin (Nucleic Acids Res., 2018)

dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. - Han Zhang 017; Tanner Yohe; Le Huang; Sarah Entwistle; Peizhi Wu; Zhenglu Yang; Peter Kamp Busk; Ying Xu; Yanbin Yin (Nucleic Acids Res., 2018)

A Meta-Path-Based Recurrent Model for Next POI Prediction with Spatial and Temporal Contexts. - Hengpeng Xu; Peizhi Wu; Jinmao Wei 001; Zhenglu Yang; Jun Wang 023 (APWeb/WAIM (2), 2019)

A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation. - Peizhi Wu; Gao Cong (SIGMOD Conference, 2021)

Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation. - Yuxing Han 002; Ziniu Wu; Peizhi Wu; Rong Zhu; Jingyi Yang; Liang Wei Tan; Kai Zeng 002; Gao Cong; Yanzhao Qin; Andreas Pfadler; Zhengping Qian; Jingren Zhou 001; et al. (Proc. VLDB Endow., 2021)

Unsupervised Selectivity Estimation by Integrating Gaussian Mixture Models and an Autoregressive Model. - Zizhong Meng; Peizhi Wu; Gao Cong; Rong Zhu; Shuai Ma 001 (EDBT, 2022)

SAM: Database Generation from Query Workloads with Supervised Autoregressive Models. - Jingyi Yang; Peizhi Wu; Gao Cong; Tieying Zhang; Xiao He 008 (SIGMOD Conference, 2022)

Adding Domain Knowledge to Query-Driven Learned Databases. - Peizhi Wu; Ryan Marcus; Zachary G. Ives (CoRR, 2023)

Modeling Shifting Workloads for Learned Database Systems. - Peizhi Wu; Zachary G. Ives (Proc. ACM Manag. Data, 2024)

QuoteInspector: Gaining Insight about Social Media Discussions. - Peizhi Wu; Yi Zhang 001; Wang-Chiew Tan; Zachary G. Ives (Proc. VLDB Endow., 2024)

A Practical Theory of Generalization in Selectivity Learning. - Peizhi Wu; Haoshu Xu; Ryan Marcus; Zack Ives (Proc. VLDB Endow., 2025)

Data-Agnostic Cardinality Learning from Imperfect Workloads. - Peizhi Wu; Rong Kang; Tieying Zhang; Jianjun Chen 001; Ryan Marcus; Zack Ives (Proc. VLDB Endow., 2025)